diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5da21b14..d2a5d4fc 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,31 +3,31 @@ repos:
     rev: v6.0.0
     hooks:
       - id: trailing-whitespace
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
       - id: end-of-file-fixer
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
       - id: check-added-large-files
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
       - id: check-merge-conflict
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
       - id: debug-statements
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
       - id: mixed-line-ending
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
 
   - repo: https://github.com/psf/black
     rev: 25.9.0
     hooks:
       - id: black
         args: ['--line-length=120', '--target-version=py311']
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
 
   - repo: https://github.com/pycqa/isort
     rev: 7.0.0
     hooks:
       - id: isort
         args: ['--profile', 'black', '--line-length', '120']
-        files: ^causal_testing/
+        files: ^(causal_testing|tests)/
 
   - repo: local
     hooks:
diff --git a/causal_testing/__main__.py b/causal_testing/__main__.py
index 845098e1..76f9e6be 100644
--- a/causal_testing/__main__.py
+++ b/causal_testing/__main__.py
@@ -1,16 +1,18 @@
 """This module contains the main entrypoint functionality to the Causal Testing Framework."""
 
 import argparse
+import json
 import logging
 from enum import Enum
 from importlib.metadata import entry_points
 from typing import Optional, Sequence
+from warnings import warn
 
 import networkx as nx
 import pandas as pd
 
 from causal_testing.causal_testing_framework import CausalTestingFramework, read_dataframe
-from causal_testing.testing.metamorphic_relation import generate_causal_tests
+from causal_testing.specification.causal_dag import CausalDAG
 
 logger = logging.getLogger(__name__)
 
@@ -23,6 +25,7 @@ class Command(Enum):
     TEST = "test"
     GENERATE = "generate"
     DISCOVER = "discover"
+    EVALUATE = "evaluate"
 
 
 def setup_logging(level: str) -> None:
@@ -40,15 +43,6 @@ def parse_args(args: Optional[Sequence[str]] = None) -> argparse.Namespace:
         "A causal inference-driven framework for functional black-box testing of complex software.",
     )
 
-    main_parser.add_argument(
-        "-l",
-        "--log_level",
-        default="WARNING",
-        type=str.upper,
-        choices=["NONE", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
-        help="Set the logging level (default: WARNING).",
-    )
-
     subparsers = main_parser.add_subparsers(
         help="The action you want to run - call `causal_testing {action} -h` for further details", dest="command"
     )
@@ -57,24 +51,6 @@ def parse_args(args: Optional[Sequence[str]] = None) -> argparse.Namespace:
     parser_generate = subparsers.add_parser(Command.GENERATE.value, help="Generate causal tests from a DAG")
     parser_generate.add_argument("-D", "--dag-path", help="Path to the DAG file (.dot)", required=True)
     parser_generate.add_argument("-o", "--output", help="Path for output file (.json)", required=True)
-    parser_generate.add_argument(
-        "-e",
-        "--estimator",
-        help="The name of the estimator class to use when evaluating tests (defaults to LinearRegressionEstimator)",
-        default="LinearRegressionEstimator",
-    )
-    parser_generate.add_argument(
-        "-T",
-        "--effect-type",
-        help="The effect type to estimate {direct, total}",
-        default="direct",
-    )
-    parser_generate.add_argument(
-        "-E",
-        "--estimate-type",
-        help="The estimate type to use when evaluating tests (defaults to coefficient)",
-        default="coefficient",
-    )
     parser_generate.add_argument(
         "-i", "--ignore-cycles", help="Ignore cycles in DAG", action="store_true", default=False
     )
@@ -87,11 +63,10 @@ def parse_args(args: Optional[Sequence[str]] = None) -> argparse.Namespace:
     parser_test.add_argument("-D", "--dag-path", help="Path to the DAG file (.dot)", required=True)
     parser_test.add_argument("-o", "--output", help="Path for output file (.json)", required=True)
     parser_test.add_argument("-i", "--ignore-cycles", help="Ignore cycles in DAG", action="store_true", default=False)
-    parser_test.add_argument("-d", "--data-paths", help="Paths to data files (.csv)", nargs="+", required=True)
     parser_test.add_argument("-t", "--test-config", help="Path to test configuration file (.json)", required=True)
     parser_test.add_argument("-q", "--query", help="Query string to filter data (e.g. 'age > 18')", type=str)
     parser_test.add_argument(
-        "-a", "--adequacy", help="Calculate causal test adequacy for each test case", action="store_true", default=False
+        "-A", "--adequacy", help="Calculate causal test adequacy for each test case", action="store_true", default=False
     )
     parser_test.add_argument(
         "-b",
@@ -108,18 +83,35 @@ def parse_args(args: Optional[Sequence[str]] = None) -> argparse.Namespace:
         default=False,
     )
 
+    # DAG evaluation
+    parser_evaluate = subparsers.add_parser(
+        Command.EVALUATE.value, help="Evaluate how well a causal DAG fits a dataset"
+    )
+    parser_evaluate.add_argument("-D", "--dag-path", help="Path to the DAG file (.dot)", required=True)
+    parser_evaluate.add_argument("-o", "--output", help="Path for output file (.csv)", required=True)
+    parser_evaluate.add_argument(
+        "-i", "--ignore-cycles", help="Ignore cycles in DAG", action="store_true", default=False
+    )
+    parser_evaluate.add_argument("-q", "--query", help="Query string to filter data (e.g. 'age > 18')", type=str)
+    parser_evaluate.add_argument(
+        "-b",
+        "--adequacy-bootstrap-size",
+        dest="bootstrap_size",
+        help="Number of bootstrap samples for causal test adequacy. Defaults to 100",
+        type=int,
+        default=100,
+    )
+    parser_evaluate.add_argument(
+        "-s",
+        "--silent",
+        action="store_true",
+        help="Do not crash on error. If set to true, errors are recorded as test results.",
+        default=False,
+    )
+    parser_evaluate.add_argument("-t", "--test-config", help="Path to test configuration file (.json)")
+
     # Discovery
     parser_discover = subparsers.add_parser(Command.DISCOVER.value, help="Discover causal structures from data")
-    parser_discover.add_argument("-d", "--data-paths", help="Paths to data files (.csv)", nargs="+", required=True)
-    parser_discover.add_argument(
-        "-a",
-        "--alpha",
-        help=(
-            "The significance level of the confidence intervals used to determine causality. "
-            "This should be a value between 0 and 1. Defaults to 0.05 for 95%% confidence intervals."
-        ),
-        default=0.05,
-    )
     parser_discover.add_argument(
         "-t",
         "--technique",
@@ -147,6 +139,26 @@ def parse_args(args: Optional[Sequence[str]] = None) -> argparse.Namespace:
         default=[],
     )
 
+    for parser in [parser_generate, parser_discover, parser_test, parser_evaluate]:
+        parser.add_argument(
+            "-l",
+            "--log_level",
+            default="WARNING",
+            type=str.upper,
+            choices=["NONE", "DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"],
+            help="Set the logging level (default: WARNING).",
+        )
+        parser.add_argument(
+            "-a",
+            "--alpha",
+            help=(
+                "The significance level of the confidence intervals used to determine causality. "
+                "This should be a value between 0 and 1. Defaults to 0.05 for 95%% confidence intervals."
+            ),
+            default=0.05,
+        )
+        parser.add_argument("-d", "--data-paths", help="Paths to data files (.csv)", nargs="+", required=True)
+
     args = main_parser.parse_args(args)
 
     # Assume the user wants test adequacy if they're setting bootstrap_size
@@ -175,16 +187,14 @@ def main() -> None:
     match args.command:
         case Command.GENERATE:
             logging.info("Generating causal tests")
-            generate_causal_tests(
-                args.dag_path,
-                args.output,
-                args.ignore_cycles,
-                args.threads,
-                effect_type=args.effect_type,
-                estimate_type=args.estimate_type,
-                estimator=args.estimator,
+            df = pd.concat(read_dataframe(path) for path in args.data_paths)
+            causal_dag = CausalDAG(args.dag_path, ignore_cycles=args.ignore_cycles, datatypes=df.dtypes)
+            causal_tests = causal_dag.generate_causal_tests(
+                threads=args.threads,
                 skip=False,
             )
+            with open(args.output, "w", encoding="utf-8") as f:
+                json.dump({"tests": [test.to_dict() for test in causal_tests]}, f)
             logging.info("Causal test generation completed successfully.")
 
         case Command.DISCOVER:
@@ -211,8 +221,8 @@ def main() -> None:
             # Drop unnamed columns
             unnamed_columns = [c for c in df.columns if c.startswith("Unnamed: ")]
             if unnamed_columns:
-                logger.warning(f"Dropping unnamed columns: {unnamed_columns}")
-            df = df.drop(unnamed_columns)
+                warn(f"Dropping unnamed columns: {unnamed_columns}")
+            df = df.drop(unnamed_columns, axis=1)
 
             discover_class = discover_map[args.technique].load()
             discover = discover_class(
@@ -246,6 +256,23 @@ def main() -> None:
             framework.save_results(args.output)
 
             logging.info("Causal testing completed successfully.")
+        case Command.EVALUATE:
+            # Create and setup framework
+            framework = CausalTestingFramework()
+            framework.load_data(args.data_paths, query=args.query)
+            framework.load_dag(args.dag_path, args.ignore_cycles)
+            framework.dag.datatypes = framework.df.dtypes
+
+            if args.test_config:
+                framework.load_test_cases_from_json(args.test_config)
+            else:
+                framework.test_cases = framework.dag.generate_causal_tests()
+
+            logging.info("Running tests on entire dataset")
+            results = framework.evaluate_dag(alpha=args.alpha, bootstrap_size=args.bootstrap_size)
+            logging.info("Causal testing completed successfully.")
+            logging.info("Running tests on bootstrap samples")
+            results.to_csv(args.output)
 
 
 if __name__ == "__main__":
diff --git a/causal_testing/causal_testing_framework.py b/causal_testing/causal_testing_framework.py
index a364036b..2a2813c5 100644
--- a/causal_testing/causal_testing_framework.py
+++ b/causal_testing/causal_testing_framework.py
@@ -7,13 +7,13 @@
 from importlib.metadata import entry_points
 from pathlib import Path
 
+import numpy as np
 import pandas as pd
 from tqdm import tqdm
 
 from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.variable import Input, Output
-from causal_testing.testing.base_test_case import BaseTestCase
 from causal_testing.testing.causal_test_case import CausalTestCase
+from causal_testing.testing.causal_test_result import TestOutcome
 
 logger = logging.getLogger(__name__)
 
@@ -58,26 +58,6 @@ def __init__(self, dag: CausalDAG = None, test_cases: list[CausalTestCase] = Non
         self.test_cases = test_cases
         self.df = df
         self.variables = {"inputs": {}, "outputs": {}}
-        if self.dag is not None and self.df is not None:
-            self.create_variables()
-
-    def create_variables(self) -> None:
-        """
-        Create variable objects from DAG nodes based on their connectivity.
-        """
-        for node_name, node_data in self.dag.nodes(data=True):
-            if node_name not in self.df.columns and not node_data.get("hidden", False):
-                raise ValueError(f"Node {node_name} missing from data. Should it be marked as hidden?")
-
-            dtype = self.df.dtypes.get(node_name)
-
-            # If node has no incoming edges, it's an input
-            if self.dag.in_degree(node_name) == 0:
-                self.variables["inputs"][node_name] = Input(name=node_name, datatype=dtype)
-
-            # Otherwise it's an output
-            if self.dag.in_degree(node_name) > 0:
-                self.variables["outputs"][node_name] = Output(name=node_name, datatype=dtype)
 
     def setup(
         self,
@@ -143,8 +123,6 @@ def load_test_cases_from_json(self, test_cases_path: str):
         if self.dag is None or self.df is None:
             raise ValueError("Please load DAG and data before attempting to load tests.")
 
-        self.create_variables()
-
         with open(test_cases_path, "r", encoding="utf-8") as f:
             test_configs = json.load(f)
 
@@ -158,32 +136,6 @@ def load_test_cases_from_json(self, test_cases_path: str):
 
         self.test_cases = test_cases
 
-    def create_base_test(self, test: dict) -> BaseTestCase:
-        """
-        Create base test case from test configuration.
-
-        :param test: Dictionary containing test configuration parameters
-
-        :return: BaseTestCase object
-        :raises: KeyError if required variables are not found in inputs or outputs
-        """
-        treatment_name = test["treatment_variable"]
-        outcome_name = next(iter(test["expected_effect"].keys()))
-
-        # Look for treatment variable in both inputs and outputs
-        treatment_var = self.variables["inputs"].get(treatment_name) or self.variables["outputs"].get(treatment_name)
-        if not treatment_var:
-            raise KeyError(f"Treatment variable '{treatment_name}' not found in inputs or outputs")
-
-        # Look for outcome variable in both inputs and outputs
-        outcome_var = self.variables["inputs"].get(outcome_name) or self.variables["outputs"].get(outcome_name)
-        if not outcome_var:
-            raise KeyError(f"Outcome variable '{outcome_name}' not found in inputs or outputs")
-
-        return BaseTestCase(
-            treatment_variable=treatment_var, outcome_variable=outcome_var, effect=test.get("effect", "total")
-        )
-
     def create_causal_test(self, test: dict) -> CausalTestCase:
         """
         Create causal test case from test configuration and base test.
@@ -196,8 +148,6 @@ def create_causal_test(self, test: dict) -> CausalTestCase:
         estimator_map = {ff.name: ff for ff in entry_points(group="estimators")}
         effect_map = {ff.name: ff for ff in entry_points(group="causal_effects")}
 
-        base_test = self.create_base_test(test)
-
         if "estimator" not in test:
             raise ValueError("Test configuration must specify an estimator")
 
@@ -209,36 +159,37 @@ def create_causal_test(self, test: dict) -> CausalTestCase:
             )
 
         # Create the estimator with correct parameters
+        treatment_variable = test.get("treatment_variable")
+        outcome_variable = test.get("outcome_variable")
         estimator_class = estimator_map.get(test["estimator"]).load()
         estimator_kwargs = test.get("estimator_kwargs", {})
+        effect_type = test.get("expected_effect", {}).get("effect_type", "direct")
+
         estimator = estimator_class(
-            base_test_case=base_test,
+            treatment_variable=treatment_variable,
+            outcome_variable=outcome_variable,
             treatment_value=test.get("treatment_value"),
             control_value=test.get("control_value"),
-            adjustment_set=test.get(
-                "adjustment_set",
-                self.dag.identification(base_test),
-            ),
             alpha=test.get("alpha", 0.05),
             **estimator_kwargs,
         )
 
         # Get effect type and create expected effect
-        effect_type = test["expected_effect"][base_test.outcome_variable.name]
+        expected_effect = test["expected_effect"]
+        effect_type = expected_effect.pop("name")
         if effect_type not in effect_map:
             raise ValueError(
                 f"Unsupported causal effect {effect_type}. Supported: {sorted(effect_map)}. "
                 "If you have implemented a custom causal effect, you will need to add this to your entrypoints via "
                 "your pyproject.toml file."
             )
-        expected_effect = effect_map[effect_type].load()(**test.get("effect_kwargs", {}))
+        expected_effect = effect_map[effect_type].load()(**expected_effect)
 
         return CausalTestCase(
             name=test.get("name"),
+            effect_measure=test.get("effect_measure"),
             query=test.get("query"),
-            base_test_case=base_test,
             expected_causal_effect=expected_effect,
-            estimate_type=test.get("estimate_type", "ate"),
             estimator=estimator,
             skip=test.get("skip", False),
         )
@@ -265,6 +216,57 @@ def run_tests(self, silent: bool = False, adequacy: bool = False, bootstrap_size
                 self.df, suppress_estimation_errors=silent, adequacy=adequacy, bootstrap_size=bootstrap_size
             )
 
+    def evaluate_dag(self, bootstrap_size: bool = 100, alpha: float = 0.05) -> pd.Series:
+        """
+        Calculate confidence intervals for how well a causal DAG fits a dataset by repeatedly resampling the dataset
+        and executing the causal tests.
+        Confidence intervals are then calcualted for how many tests pass, fail, or are inestimable.
+
+        :param bootstrap_size: The number of bootstrap samples to use when calculating causal test adequacy
+                               (defaults to 100)
+        :param alpha: The significance level to use when calculating confidence intervals.
+                      (defaults to 0.05).
+        """
+        self.run_tests(silent=True, adequacy=False)
+        results = {
+            test_outcome.name: len(
+                [test for test in self.test_cases if test.result and test.result.outcome == test_outcome]
+            )
+            for test_outcome in TestOutcome
+        }
+
+        sample_results = []
+        for sample_index in range(bootstrap_size):
+            test_outcomes = {test_outcome: 0 for test_outcome in TestOutcome}
+            for test_case in self.test_cases:
+                if test_case.skip:
+                    continue
+                try:
+                    effect_estimate = test_case.estimate_effect(
+                        df=self.df.sample(len(self.df), replace=True, random_state=sample_index)
+                    )
+                except (np.linalg.LinAlgError, ValueError):
+                    test_outcomes[TestOutcome.INESTIMABLE] += 1
+
+                if effect_estimate:
+                    if test_case.expected_causal_effect.apply(effect_estimate):
+                        test_outcomes[TestOutcome.PASS] += 1
+                    else:
+                        test_outcomes[TestOutcome.FAIL] += 1
+            sample_results.append(test_outcomes)
+
+        sample_results = pd.DataFrame(sample_results)
+
+        # Calculate the confidence interval of each column
+        ci_low_inx = round((alpha / 2) * bootstrap_size)
+        ci_high_inx = round(((1 - alpha) / 2) * bootstrap_size)
+        for outcome in TestOutcome:
+            data = sorted(sample_results[outcome])
+            results[f"{outcome.name}_ci_low"] = data[ci_low_inx]
+            results[f"{outcome.name}_ci_high"] = data[ci_high_inx]
+
+        return pd.Series(results).sort_index()
+
     def save_results(self, output_path) -> list:
         """Save test results to JSON file in the expected format."""
         logger.info(f"Saving results to {output_path}")
@@ -272,64 +274,8 @@ def save_results(self, output_path) -> list:
         # Create parent directory if it doesn't exist
         Path(output_path).parent.mkdir(parents=True, exist_ok=True)
 
-        json_results = []
-        result_index = 0
-
-        for test_case in self.test_cases:
-            # Create a base output first of common entries
-            base_output = {
-                "name": test_case.name,
-                "estimate_type": test_case.estimate_type,
-                "effect": test_case.base_test_case.effect,
-                "treatment_variable": test_case.base_test_case.treatment_variable.name,
-                "expected_effect": test_case.expected_causal_effect.__class__.__name__,
-                "alpha": test_case.estimator.alpha,
-            }
-            if test_case.skip:
-                # Include those skipped test entry without execution results
-                output = {
-                    **base_output,
-                    "formula": test_case.estimator.formula if hasattr(test_case.estimator, "formula") else None,
-                    "skip": True,
-                    "passed": None,
-                    "result": {
-                        "status": "skipped",
-                        "reason": "Test marked as skip:true in the causal test config file.",
-                    },
-                }
-            else:
-                result = test_case.result
-                result_index += 1
-
-                test_passed = (
-                    test_case.expected_causal_effect.apply(result) if result.effect_estimate is not None else False
-                )
-
-                output = {
-                    **base_output,
-                    "formula": test_case.estimator.formula if hasattr(test_case.estimator, "formula") else None,
-                    "skip": False,
-                    "passed": test_passed,
-                    "result": (
-                        {
-                            "treatment": test_case.estimator.base_test_case.treatment_variable.name,
-                            "outcome": test_case.estimator.base_test_case.outcome_variable.name,
-                            "adjustment_set": (
-                                list(test_case.estimator.adjustment_set) if test_case.estimator.adjustment_set else []
-                            ),
-                        }
-                        | result.effect_estimate.to_dict()
-                        | (result.adequacy.to_dict() if result.adequacy else {})
-                        if result.effect_estimate
-                        else {"status": "error", "reason": result.error_message}
-                    ),
-                }
-
-            json_results.append(output)
-
         # Save to file
         with open(output_path, "w", encoding="utf-8") as f:
-            json.dump(json_results, f, indent=2)
+            json.dump([test.to_dict() for test in self.test_cases], f, indent=2)
 
         logger.info("Results saved successfully")
-        return json_results
diff --git a/causal_testing/discovery/abstract_discovery.py b/causal_testing/discovery/abstract_discovery.py
index ee599ad9..c0b51387 100644
--- a/causal_testing/discovery/abstract_discovery.py
+++ b/causal_testing/discovery/abstract_discovery.py
@@ -6,7 +6,6 @@
 import re
 import warnings
 from abc import ABC, abstractmethod
-from enum import Enum
 from itertools import permutations
 
 import networkx as nx
@@ -16,12 +15,9 @@
 
 from causal_testing.causal_testing_framework import CausalTestingFramework
 from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.scenario import Scenario
 from causal_testing.testing.causal_effect import Negative, Positive
 from causal_testing.testing.causal_test_case import CausalTestCase
-from causal_testing.testing.metamorphic_relation import generate_metamorphic_relations
-
-TestResult = Enum("TestResult", [("PASS", 2), ("FAIL", 0), ("INESTIMABLE", 1)])
+from causal_testing.testing.causal_test_result import TestOutcome
 
 # Ignore warnings from statsmodels when we try to evaluate test cases
 warnings.simplefilter("ignore")
@@ -108,12 +104,12 @@ def effect_direction(self, test_case: CausalTestCase) -> str:
         :param test_case: The causal test case.
         :returns: Whether the estimated causal test is positive or negative (or no effect).
         """
-        if pd.api.types.is_numeric_dtype(
-            self.df[test_case.base_test_case.treatment_variable.name]
-        ) and pd.api.types.is_numeric_dtype(self.df[test_case.base_test_case.outcome_variable.name]):
-            if Negative().apply(test_case.result):
+        if pd.api.types.is_numeric_dtype(self.df[test_case.treatment_variable]) and pd.api.types.is_numeric_dtype(
+            self.df[test_case.outcome_variable]
+        ):
+            if Negative().apply(test_case.result.effect_estimate):
                 return "negative"
-            if Positive().apply(test_case.result):
+            if Positive().apply(test_case.result.effect_estimate):
                 return "positive"
         return None
 
@@ -148,15 +144,15 @@ def write_dot(self, individual: CausalDAG, output_file: str):
 
                     print(test)
 
-                    if test["result"] == TestResult.PASS:
+                    if test["result"] == TestOutcome.PASS:
                         print("  GREEN")
                         individual[test["treatment"]][test["outcome"]]["color"] = "green"
                         individual[test["treatment"]][test["outcome"]]["fontcolor"] = "green"
-                    elif test["result"] == TestResult.INESTIMABLE:
+                    elif test["result"] == TestOutcome.INESTIMABLE:
                         print("  ORANGE")
                         individual[test["treatment"]][test["outcome"]]["color"] = "orange"
                         individual[test["treatment"]][test["outcome"]]["fontcolor"] = "orange"
-                    elif test["result"] == TestResult.FAIL:
+                    elif test["result"] == TestOutcome.FAIL:
                         print("  RED")
                         individual[test["treatment"]][test["outcome"]]["color"] = "red"
                         individual[test["treatment"]][test["outcome"]]["fontcolor"] = "red"
@@ -166,13 +162,13 @@ def write_dot(self, individual: CausalDAG, output_file: str):
                     individual.add_edge(test["treatment"], test["outcome"], ignore_cycles=True)
                     individual[test["treatment"]][test["outcome"]]["style"] = "dashed"
                     individual[test["treatment"]][test["outcome"]]["label"] = test["effect"]
-                    if test["result"] == TestResult.PASS:
+                    if test["result"] == TestOutcome.PASS:
                         individual[test["treatment"]][test["outcome"]]["style"] = "invis"
                         individual[test["treatment"]][test["outcome"]]["constraint"] = False
-                    elif test["result"] == TestResult.INESTIMABLE:
+                    elif test["result"] == TestOutcome.INESTIMABLE:
                         individual[test["treatment"]][test["outcome"]]["color"] = "orange"
                         individual[test["treatment"]][test["outcome"]]["fontcolor"] = "orange"
-                    elif test["result"] == TestResult.FAIL:
+                    elif test["result"] == TestOutcome.FAIL:
                         individual[test["treatment"]][test["outcome"]]["color"] = "red"
                         individual[test["treatment"]][test["outcome"]]["fontcolor"] = "red"
                     else:
@@ -180,15 +176,6 @@ def write_dot(self, individual: CausalDAG, output_file: str):
 
         nx.drawing.nx_pydot.write_dot(individual, output_file)
 
-    def _json_stub_params(self, outcome: str) -> str:
-        if pd.api.types.is_bool_dtype(self.df[outcome]):
-            return {"estimator": "LogisticRegressionEstimator", "estimate_type": "unit_odds_ratio"}
-        if pd.api.types.is_categorical_dtype(self.df[outcome]) or pd.api.types.is_object_dtype(self.df[outcome]):
-            return {"estimator": "MultinomialRegressionEstimator", "estimate_type": "unit_odds_ratio"}
-        if pd.api.types.is_numeric_dtype(self.df[outcome]):
-            return {"estimator": "LinearRegressionEstimator", "estimate_type": "coefficient"}
-        raise ValueError(f"Invalid datatype {self.df.dtypes[outcome]}")
-
     def evaluate_tests(self, causal_dag: CausalDAG) -> pd.DataFrame:
         """
         Generate and evaluate causal test cases from the supplied CausalDAG and return a list of edges for which the
@@ -201,18 +188,8 @@ def evaluate_tests(self, causal_dag: CausalDAG) -> pd.DataFrame:
         """
 
         ctf = CausalTestingFramework(dag=causal_dag, df=self.df)
-        ctf.create_variables()
-        ctf.scenario = Scenario(list(ctf.variables["inputs"].values()) + list(ctf.variables["outputs"].values()))
-
-        ctf.test_cases = [
-            ctf.create_causal_test(
-                relation.to_json_stub(
-                    alpha=self.alpha,
-                    **self._json_stub_params(relation.base_test_case.outcome_variable),
-                )
-            )
-            for relation in generate_metamorphic_relations(causal_dag)
-        ]
+        causal_dag.datatypes = self.df.dtypes
+        ctf.test_cases = causal_dag.generate_causal_tests()
 
         results = []
 
@@ -222,23 +199,23 @@ def evaluate_tests(self, causal_dag: CausalDAG) -> pd.DataFrame:
                 results.append(
                     {
                         "result": (
-                            TestResult.PASS
-                            if test_case.expected_causal_effect.apply(test_case.result)
-                            else TestResult.FAIL
+                            TestOutcome.PASS
+                            if test_case.expected_causal_effect.apply(test_case.result.effect_estimate)
+                            else TestOutcome.FAIL
                         ),
                         "expected_effect": test_case.expected_causal_effect.__class__.__name__,
-                        "treatment": test_case.base_test_case.treatment_variable.name,
-                        "outcome": test_case.base_test_case.outcome_variable.name,
+                        "treatment": test_case.treatment_variable,
+                        "outcome": test_case.outcome_variable,
                         "effect": self.effect_direction(test_case),
                     }
                 )
             except np.linalg.LinAlgError:
                 results.append(
                     {
-                        "result": TestResult.INESTIMABLE,
+                        "result": TestOutcome.INESTIMABLE,
                         "expected_effect": test_case.expected_causal_effect.__class__.__name__,
-                        "treatment": test_case.base_test_case.treatment_variable.name,
-                        "outcome": test_case.base_test_case.outcome_variable.name,
+                        "treatment": test_case.treatment_variable,
+                        "outcome": test_case.outcome_variable,
                     }
                 )
 
diff --git a/causal_testing/discovery/hill_climber_discovery.py b/causal_testing/discovery/hill_climber_discovery.py
index c9e057bb..3faf506a 100644
--- a/causal_testing/discovery/hill_climber_discovery.py
+++ b/causal_testing/discovery/hill_climber_discovery.py
@@ -8,8 +8,9 @@
 import numpy as np
 import pandas as pd
 
-from causal_testing.discovery.abstract_discovery import Discovery, TestResult
+from causal_testing.discovery.abstract_discovery import Discovery
 from causal_testing.specification.causal_dag import CausalDAG
+from causal_testing.testing.causal_test_result import TestOutcome
 
 
 class HillClimberDiscovery(Discovery):
@@ -48,10 +49,10 @@ def sum_test_outcomes(self, test_results: pd.DataFrame) -> dict:
             axis=1,
         )
         # Ensure every column is initialised - Test outcomes that never occurred won't be in the dataframe otherwise
-        for col in TestResult:
+        for col in TestOutcome:
             if col not in counts.columns:
                 counts[col] = 0
-        counts = counts.groupby(["treatment", "outcome"]).sum().reset_index()[list(TestResult)]
+        counts = counts.groupby(["treatment", "outcome"]).sum().reset_index()[list(TestOutcome)]
         # The below line normalises by the number of tests *for each edge*
         # Independence tests X _||_ Y get two tests (X _||_ Y and Y _||_ X) because we don't know which way the
         # causality flows. We need to normalise this (e.g. if X _||_ Y and Y _||_ X both pass, then the score should be
@@ -89,15 +90,15 @@ def evaluate_fitness(
         )
         problem_tests = query_df.groupby(["var1", "var2"]).filter(
             # Groups are problematic if at least one test fails or no test passes
-            lambda group: (group["result"] == TestResult.FAIL).any()
-            or ~(group["result"] == TestResult.PASS).any()
+            lambda group: (group["result"] == TestOutcome.FAIL).any()
+            or ~(group["result"] == TestOutcome.PASS).any()
         )
         problem_edges = problem_tests[["treatment", "outcome"]].apply(tuple, axis=1).tolist()
 
         fitness_values = (
-            counts.get(TestResult.PASS, 0),
-            -counts.get(TestResult.FAIL, 0),
-            -counts.get(TestResult.INESTIMABLE, 0),
+            counts.get(TestOutcome.PASS, 0),
+            -counts.get(TestOutcome.FAIL, 0),
+            -counts.get(TestOutcome.INESTIMABLE, 0),
         )
         return fitness_values, problem_edges
 
diff --git a/causal_testing/estimation/abstract_estimator.py b/causal_testing/estimation/abstract_estimator.py
index 0ffdadf5..d678c06d 100644
--- a/causal_testing/estimation/abstract_estimator.py
+++ b/causal_testing/estimation/abstract_estimator.py
@@ -4,8 +4,6 @@
 from abc import ABC, abstractmethod
 from typing import Any
 
-from causal_testing.testing.base_test_case import BaseTestCase
-
 logger = logging.getLogger(__name__)
 
 
@@ -31,26 +29,22 @@ def __init__(
         # pylint: disable=too-many-arguments
         # pylint: disable=R0801
         self,
-        base_test_case: BaseTestCase,
-        treatment_value: float,
-        control_value: float,
-        adjustment_set: set,
-        effect_modifiers: dict[str, Any] = None,
+        treatment_variable: str,
+        outcome_variable: str,
+        control_value: float = None,
+        treatment_value: float = None,
+        adjustment_config: dict[str, Any] = None,
         alpha: float = 0.05,
     ):
-        self.base_test_case = base_test_case
+
+        self.treatment_variable = treatment_variable
+        self.outcome_variable = outcome_variable
         self.treatment_value = treatment_value
         self.control_value = control_value
-        self.adjustment_set = adjustment_set
         self.alpha = alpha
-
-        if effect_modifiers is None:
-            self.effect_modifiers = {}
-        else:
-            self.effect_modifiers = effect_modifiers
+        self.adjustment_config = {} if adjustment_config is None else adjustment_config
         self.modelling_assumptions = []
         self.add_modelling_assumptions()
-        logger.debug("Effect Modifiers: %s", self.effect_modifiers)
 
     @abstractmethod
     def add_modelling_assumptions(self):
@@ -58,3 +52,24 @@ def add_modelling_assumptions(self):
         Add modelling assumptions to the estimator. This is a list of strings which list the modelling assumptions that
         must hold if the resulting causal inference is to be considered valid.
         """
+
+    def to_dict(self) -> dict:
+        """
+        Convert the estimator to a python dictionary for easy serialisation as JSON or CSV.
+
+        :returns: A JSON serialisable dict representing the estimator.
+        """
+        result = {
+            "name": self.__class__.__name__,
+            "treatment_variable": self.treatment_variable,
+            "outcome_variable": self.outcome_variable,
+            "alpha": self.alpha,
+        }
+
+        if self.adjustment_config:
+            result["adjustment_config"] = self.adjustment_config
+        if self.control_value is not None:
+            result["control_value"] = self.control_value
+        if self.treatment_value is not None:
+            result["treatment_value"] = self.treatment_value
+        return result
diff --git a/causal_testing/estimation/abstract_regression_estimator.py b/causal_testing/estimation/abstract_regression_estimator.py
index cebfe727..cee528b0 100644
--- a/causal_testing/estimation/abstract_regression_estimator.py
+++ b/causal_testing/estimation/abstract_regression_estimator.py
@@ -1,16 +1,15 @@
 """This module contains the RegressionEstimator, which is an abstract class for concrete regression estimators."""
 
+import ast
 import logging
 from abc import abstractmethod
 from typing import Any
 
 import pandas as pd
-from patsy import dmatrices, dmatrix  # pylint: disable = no-name-in-module
+from patsy import ModelDesc, build_design_matrices, dmatrices  # pylint: disable = no-name-in-module
 from statsmodels.regression.linear_model import RegressionResultsWrapper
 
 from causal_testing.estimation.abstract_estimator import Estimator
-from causal_testing.specification.variable import Variable
-from causal_testing.testing.base_test_case import BaseTestCase
 
 logger = logging.getLogger(__name__)
 
@@ -23,40 +22,86 @@ class RegressionEstimator(Estimator):
     def __init__(
         # pylint: disable=too-many-arguments
         self,
-        base_test_case: BaseTestCase,
-        treatment_value: float = None,
+        treatment_variable: str,
+        outcome_variable: str,
         control_value: float = None,
-        adjustment_set: set = None,
-        effect_modifiers: dict[Variable, Any] = None,
-        adjustment_config: dict[Variable, Any] = None,
+        treatment_value: float = None,
+        adjustment_set: set[str] = None,
+        adjustment_config: dict[str, Any] = None,
         formula: str = None,
         alpha: float = 0.05,
     ):
         # pylint: disable=R0801
         super().__init__(
-            base_test_case=base_test_case,
-            treatment_value=treatment_value,
+            treatment_variable=treatment_variable,
+            outcome_variable=outcome_variable,
             control_value=control_value,
-            adjustment_set=adjustment_set,
-            effect_modifiers=effect_modifiers,
+            treatment_value=treatment_value,
             alpha=alpha,
         )
-
-        if effect_modifiers is None:
-            effect_modifiers = {}
-        self.adjustment_config = {} if adjustment_config is None else adjustment_config
-        if adjustment_set is None:
-            adjustment_set = []
         if formula is not None:
             self.formula = formula
+            self._adjustment_set_from_formula()
+            if adjustment_set is not None and set(adjustment_set) != set(self.adjustment_set):
+                raise ValueError(
+                    f"Specified formula {self.formula} does not match specified adjustment set {adjustment_set}"
+                )
+        elif adjustment_set is not None:
+            self.adjustment_set = adjustment_set
+            terms = [treatment_variable] + sorted(list(adjustment_set))
+            self.formula = f"{outcome_variable} ~ {' + '.join(terms)}"
         else:
-            terms = (
-                [base_test_case.treatment_variable.name] + sorted(list(adjustment_set)) + sorted(list(effect_modifiers))
+            raise ValueError("Please specify either a formula or an adjustment set.")
+
+        self.adjustment_config = adjustment_config if adjustment_config is not None else {}
+        if not set(self.adjustment_config).issubset(self.adjustment_set):
+            raise ValueError(
+                "Specified configuration for variables "
+                f"{sorted([v for v in adjustment_config if v not in self.adjustment_set])} "
+                f"which are not in the adjustment set {self.adjustment_set}."
             )
-            self.formula = f"{base_test_case.outcome_variable.name} ~ {'+'.join(terms)}"
 
-        for term in list(self.effect_modifiers) + list(self.adjustment_config):
-            self.adjustment_set.add(term)
+    def _get_adjusted_variables(self, tree: ast.AST) -> set[str]:
+        """
+        Recursively return variables in an AST.
+        :returns: Set of all variables not used as part of a function.
+        """
+        if isinstance(tree, ast.Name) and tree.id != self.treatment_variable:
+            return {tree.id}
+        if isinstance(tree, ast.Expression):
+            return self._get_adjusted_variables(tree.body)
+        if isinstance(tree, ast.Call):
+            return set().union(*[self._get_adjusted_variables(arg) for arg in tree.args])
+        if isinstance(tree, ast.BinOp):
+            return self._get_adjusted_variables(tree.left).union(self._get_adjusted_variables(tree.right))
+        return set()
+
+    def _adjustment_set_from_formula(self):
+        """
+        Set up the adjustment set from the formula string.
+        """
+        desc = ModelDesc.from_formula(self.formula)
+
+        # Check that the outcome variable is the dependent variable specified in the formula
+        if desc.lhs_termlist:
+            if [self.outcome_variable] != [term.name() for term in desc.lhs_termlist]:
+                raise ValueError(
+                    f"Left hand side of formula {self.formula} does not match the specified outcome_variable "
+                    f"{self.outcome_variable}."
+                )
+        # If no dependent variable is specified, make it the outcome variable
+        else:
+            self.formula = f"{self.outcome_variable} ~ {self.formula}"
+
+        raw_factors = {factor.code for term in desc.rhs_termlist for factor in term.factors}
+
+        adjustment_set = set()
+
+        for code in raw_factors:
+            tree = ast.parse(code, mode="eval")
+            adjustment_set = adjustment_set.union(self._get_adjusted_variables(tree))
+
+        self.adjustment_set = adjustment_set
 
     def _setup_covariates(self, df: pd.DataFrame) -> pd.Series:
         """
@@ -69,7 +114,7 @@ def _setup_covariates(self, df: pd.DataFrame) -> pd.Series:
         _, covariate_data = dmatrices(self.formula, df, return_type="dataframe")
         df = pd.concat([df, covariate_data[[col for col in covariate_data.columns if col not in df]]], axis=1)
         covariates = covariate_data.columns.tolist()
-        return covariates, df.dropna(subset=covariates)
+        return df.dropna(subset=covariates), covariates, covariate_data.design_info
 
     @property
     @abstractmethod
@@ -98,8 +143,9 @@ def fit_model(self, df: pd.DataFrame) -> RegressionResultsWrapper:
         :param df: The data to use.
         :return: The model after fitting to data.
         """
-        covariates, df = self._setup_covariates(df)
-        model = self.regressor(df[self.base_test_case.outcome_variable.name], df[covariates]).fit(disp=0)
+        df, covariates, design_info = self._setup_covariates(df)
+        model = self.regressor(df[self.outcome_variable], df[covariates]).fit(disp=0)
+        model.design_info = design_info
         return model
 
     def treatment_columns(self, model: RegressionResultsWrapper) -> list[str]:
@@ -115,8 +161,7 @@ def treatment_columns(self, model: RegressionResultsWrapper) -> list[str]:
         return [
             param
             for param in model.params.index
-            if param == self.base_test_case.treatment_variable.name
-            or param.startswith(self.base_test_case.treatment_variable.name + "[")
+            if param == self.treatment_variable or param.startswith(self.treatment_variable + "[")
         ]
 
     def _predict(self, df) -> pd.DataFrame:
@@ -132,15 +177,25 @@ def _predict(self, df) -> pd.DataFrame:
 
         x = pd.DataFrame(columns=df.columns)
         x["Intercept"] = 1  # self.intercept
-        x[self.base_test_case.treatment_variable.name] = [self.treatment_value, self.control_value]
+        x[self.treatment_variable] = [self.treatment_value, self.control_value]
 
         for k, v in self.adjustment_config.items():
             x[k] = v
-        for k, v in self.effect_modifiers.items():
-            x[k] = v
-        x = dmatrix(self.formula.split("~")[1], x, return_type="dataframe")
-        for col in x:
-            if str(x.dtypes[col]) == "object":
-                x = pd.get_dummies(x, columns=[col], drop_first=True)
+        x = build_design_matrices([model.design_info], x, return_type="dataframe")[0]
 
         return model.get_prediction(x).summary_frame()
+
+    def to_dict(self) -> dict:
+        """
+        Convert the estimator to a python dictionary for easy serialisation as JSON or CSV.
+
+        :returns: A JSON serialisable dict representing the estimator.
+        """
+        result = super().to_dict()
+        if self.adjustment_set:
+            result["adjustment_set"] = sorted(self.adjustment_set)
+        if self.adjustment_config:
+            result["adjustment_config"] = self.adjustment_config
+        if self.formula:
+            result["formula"] = self.formula
+        return result
diff --git a/causal_testing/estimation/cubic_spline_estimator.py b/causal_testing/estimation/cubic_spline_estimator.py
deleted file mode 100644
index a5a53705..00000000
--- a/causal_testing/estimation/cubic_spline_estimator.py
+++ /dev/null
@@ -1,99 +0,0 @@
-"""This module contains the CubicSplineRegressionEstimator class, for estimating
-continuous outcomes with changes in behaviour"""
-
-import logging
-from typing import Any
-
-import pandas as pd
-import statsmodels.formula.api as smf
-from statsmodels.regression.linear_model import RegressionResultsWrapper
-
-from causal_testing.estimation.effect_estimate import EffectEstimate
-from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
-from causal_testing.specification.variable import Variable
-from causal_testing.testing.base_test_case import BaseTestCase
-
-logger = logging.getLogger(__name__)
-
-
-class CubicSplineRegressionEstimator(LinearRegressionEstimator):
-    """A Cubic Spline Regression Estimator is a parametric estimator which restricts the variables in the data to a
-    combination of parameters and basis functions of the variables.
-    """
-
-    regressor = smf.ols
-
-    def __init__(
-        # pylint: disable=too-many-arguments
-        self,
-        base_test_case: BaseTestCase,
-        treatment_value: float,
-        control_value: float,
-        adjustment_set: set,
-        basis: int,
-        effect_modifiers: dict[Variable, Any] = None,
-        formula: str = None,
-        alpha: float = 0.05,
-        expected_relationship=None,
-        adjustment_config: dict[Variable, Any] = None,
-    ):
-        super().__init__(
-            base_test_case=base_test_case,
-            treatment_value=treatment_value,
-            control_value=control_value,
-            adjustment_set=adjustment_set,
-            effect_modifiers=effect_modifiers,
-            formula=formula,
-            alpha=alpha,
-        )
-
-        self.expected_relationship = expected_relationship
-        self.adjustment_config = adjustment_config
-
-        if effect_modifiers is None:
-            effect_modifiers = {}
-
-        if formula is None:
-            terms = (
-                [base_test_case.treatment_variable.name] + sorted(list(adjustment_set)) + sorted(list(effect_modifiers))
-            )
-            self.formula = f"{base_test_case.outcome_variable.name} ~ cr({'+'.join(terms)}, df={basis})"
-
-    def fit_model(self, df: pd.DataFrame) -> RegressionResultsWrapper:
-        """Run linear regression of the treatment and adjustment set against the outcome and return the model.
-
-        :param df: The data to use.
-        :return: The model after fitting to data.
-        """
-        model = self.regressor(formula=self.formula, data=df).fit(disp=0)
-        return model
-
-    def estimate_ate_calculated(
-        self,
-        df: pd.DataFrame,
-    ) -> EffectEstimate:
-        """Estimate the ate effect of the treatment on the outcome. That is, the change in outcome caused
-        by changing the treatment variable from the control value to the treatment value. Here, we actually
-        calculate the expected outcomes under control and treatment and divide one by the other. This
-        allows for custom terms to be put in such as squares, inverses, products, etc.
-
-        :param df: The data to use.
-
-        :return: The average treatment effect.
-        """
-        model = self.fit_model(df)
-
-        x = pd.DataFrame({"Intercept": [1], self.base_test_case.treatment_variable.name: [self.treatment_value]})
-        if self.adjustment_config is not None:
-            for k, v in self.adjustment_config.items():
-                x[k] = v
-        if self.effect_modifiers is not None:
-            for k, v in self.effect_modifiers.items():
-                x[k] = v
-
-        treatment = model.predict(x).iloc[0]
-
-        x[self.base_test_case.treatment_variable.name] = self.control_value
-        control = model.predict(x).iloc[0]
-
-        return EffectEstimate("ate", pd.Series(treatment - control))
diff --git a/causal_testing/estimation/experimental_estimator.py b/causal_testing/estimation/experimental_estimator.py
index f8ed77a8..773a5703 100644
--- a/causal_testing/estimation/experimental_estimator.py
+++ b/causal_testing/estimation/experimental_estimator.py
@@ -7,7 +7,6 @@
 
 from causal_testing.estimation.abstract_estimator import Estimator
 from causal_testing.estimation.effect_estimate import EffectEstimate
-from causal_testing.testing.base_test_case import BaseTestCase
 
 
 class ExperimentalEstimator(Estimator):
@@ -19,25 +18,23 @@ class ExperimentalEstimator(Estimator):
     def __init__(
         # pylint: disable=too-many-arguments
         self,
-        base_test_case: BaseTestCase,
+        treatment_variable: str,
+        outcome_variable: str,
         treatment_value: float,
         control_value: float,
-        adjustment_set: dict[str, Any],
-        effect_modifiers: dict[str, Any] = None,
+        adjustment_config: dict[str, Any],
         alpha: float = 0.05,
         repeats: int = 200,
     ):
         # pylint: disable=R0801
         super().__init__(
-            base_test_case=base_test_case,
+            treatment_variable=treatment_variable,
+            outcome_variable=outcome_variable,
             treatment_value=treatment_value,
             control_value=control_value,
-            adjustment_set=adjustment_set,
-            effect_modifiers=effect_modifiers,
+            adjustment_config=adjustment_config,
             alpha=alpha,
         )
-        if effect_modifiers is None:
-            self.effect_modifiers = {}
         self.repeats = repeats
 
     def add_modelling_assumptions(self):
@@ -63,25 +60,14 @@ def estimate_ate(self) -> EffectEstimate:
 
         :return: The average treatment effect and the bootstrapped confidence intervals.
         """
-        control_configuration = (
-            self.adjustment_set
-            | self.effect_modifiers
-            | {self.base_test_case.treatment_variable.name: self.control_value}
-        )
-        treatment_configuration = (
-            self.adjustment_set
-            | self.effect_modifiers
-            | {self.base_test_case.treatment_variable.name: self.treatment_value}
-        )
+        control_configuration = self.adjustment_config | {self.treatment_variable: self.control_value}
+        treatment_configuration = self.adjustment_config | {self.treatment_variable: self.treatment_value}
 
         control_outcomes = pd.DataFrame([self.run_system(control_configuration) for _ in range(self.repeats)])
         treatment_outcomes = pd.DataFrame([self.run_system(treatment_configuration) for _ in range(self.repeats)])
 
         difference = (
-            (
-                treatment_outcomes[self.base_test_case.outcome_variable.name]
-                - control_outcomes[self.base_test_case.outcome_variable.name]
-            )
+            (treatment_outcomes[self.outcome_variable] - control_outcomes[self.outcome_variable])
             .sort_values()
             .reset_index()
         )
@@ -92,17 +78,9 @@ def estimate_ate(self) -> EffectEstimate:
 
         return EffectEstimate(
             "ate",
-            pd.Series(
-                {
-                    self.base_test_case.treatment_variable.name: difference.mean()[
-                        self.base_test_case.outcome_variable.name
-                    ]
-                }
-            ),
-            pd.Series({self.base_test_case.treatment_variable.name: ci_low[self.base_test_case.outcome_variable.name]}),
-            pd.Series(
-                {self.base_test_case.treatment_variable.name: ci_high[self.base_test_case.outcome_variable.name]}
-            ),
+            pd.Series({self.treatment_variable: difference.mean()[self.outcome_variable]}),
+            pd.Series({self.treatment_variable: ci_low[self.outcome_variable]}),
+            pd.Series({self.treatment_variable: ci_high[self.outcome_variable]}),
         )
 
     def estimate_risk_ratio(self) -> tuple[pd.Series, list[pd.Series, pd.Series]]:
@@ -111,25 +89,14 @@ def estimate_risk_ratio(self) -> tuple[pd.Series, list[pd.Series, pd.Series]]:
 
         :return: The average treatment effect and the bootstrapped confidence intervals.
         """
-        control_configuration = (
-            self.adjustment_set
-            | self.effect_modifiers
-            | {self.base_test_case.treatment_variable.name: self.control_value}
-        )
-        treatment_configuration = (
-            self.adjustment_set
-            | self.effect_modifiers
-            | {self.base_test_case.treatment_variable.name: self.treatment_value}
-        )
+        control_configuration = self.adjustment_config | {self.treatment_variable: self.control_value}
+        treatment_configuration = self.adjustment_config | {self.treatment_variable: self.treatment_value}
 
         control_outcomes = pd.DataFrame([self.run_system(control_configuration) for _ in range(self.repeats)])
         treatment_outcomes = pd.DataFrame([self.run_system(treatment_configuration) for _ in range(self.repeats)])
 
         difference = (
-            (
-                treatment_outcomes[self.base_test_case.outcome_variable.name]
-                / control_outcomes[self.base_test_case.outcome_variable.name]
-            )
+            (treatment_outcomes[self.outcome_variable] / control_outcomes[self.outcome_variable])
             .sort_values()
             .reset_index()
         )
@@ -140,9 +107,7 @@ def estimate_risk_ratio(self) -> tuple[pd.Series, list[pd.Series, pd.Series]]:
 
         return EffectEstimate(
             "ate",
-            {self.base_test_case.treatment_variable.name: difference.mean()[self.base_test_case.outcome_variable.name]},
-            pd.Series({self.base_test_case.treatment_variable.name: ci_low[self.base_test_case.outcome_variable.name]}),
-            pd.Series(
-                {self.base_test_case.treatment_variable.name: ci_high[self.base_test_case.outcome_variable.name]}
-            ),
+            {self.treatment_variable: difference.mean()[self.outcome_variable]},
+            pd.Series({self.treatment_variable: ci_low[self.outcome_variable]}),
+            pd.Series({self.treatment_variable: ci_high[self.outcome_variable]}),
         )
diff --git a/causal_testing/estimation/instrumental_variable_estimator.py b/causal_testing/estimation/instrumental_variable_estimator.py
index 8ff237ee..f4ffab37 100644
--- a/causal_testing/estimation/instrumental_variable_estimator.py
+++ b/causal_testing/estimation/instrumental_variable_estimator.py
@@ -9,7 +9,6 @@
 
 from causal_testing.estimation.abstract_estimator import Estimator
 from causal_testing.estimation.effect_estimate import EffectEstimate
-from causal_testing.testing.base_test_case import BaseTestCase
 
 logger = logging.getLogger(__name__)
 
@@ -24,20 +23,19 @@ def __init__(
         # pylint: disable=too-many-arguments
         # pylint: disable=duplicate-code
         self,
-        base_test_case: BaseTestCase,
+        outcome_variable: str,
+        treatment_variable: str,
         treatment_value: float,
         control_value: float,
-        adjustment_set: set,
         instrument: str,
         alpha: float = 0.05,
         bootstrap_size=100,
     ):
         super().__init__(
-            base_test_case=base_test_case,
+            treatment_variable=treatment_variable,
+            outcome_variable=outcome_variable,
             treatment_value=treatment_value,
             control_value=control_value,
-            adjustment_set=adjustment_set,
-            effect_modifiers=None,
             alpha=alpha,
         )
 
@@ -49,17 +47,13 @@ def add_modelling_assumptions(self):
         Add modelling assumptions to the estimator. This is a list of strings which list the modelling assumptions that
         must hold if the resulting causal inference is to be considered valid.
         """
-        self.modelling_assumptions.append(
-            """The instrument and the treatment, and the treatment and the outcome must be
-        related linearly in the form Y = aX + b."""
-        )
-        self.modelling_assumptions.append(
-            """The three IV conditions must hold
+        self.modelling_assumptions.append("""The instrument and the treatment, and the treatment and the outcome must be
+        related linearly in the form Y = aX + b.""")
+        self.modelling_assumptions.append("""The three IV conditions must hold
             (i) Instrument is associated with treatment
             (ii) Instrument does not affect outcome except through its potential effect on treatment
             (iii) Instrument and outcome do not share causes
-        """
-        )
+        """)
 
     def iv_coefficient(self, df) -> float:
         """
@@ -67,10 +61,10 @@ def iv_coefficient(self, df) -> float:
         outcome.
         """
         # Estimate the total effect of instrument I on outcome Y = abI + c1
-        ab = sm.OLS(df[self.base_test_case.outcome_variable.name], df[[self.instrument]]).fit().params[self.instrument]
+        ab = sm.OLS(df[self.outcome_variable], df[[self.instrument]]).fit().params[self.instrument]
 
         # Estimate the direct effect of instrument I on treatment X = aI + c1
-        a = sm.OLS(df[self.base_test_case.treatment_variable.name], df[[self.instrument]]).fit().params[self.instrument]
+        a = sm.OLS(df[self.treatment_variable], df[[self.instrument]]).fit().params[self.instrument]
 
         # Estimate the coefficient of I on X by cancelling
         return ab / a
@@ -87,3 +81,11 @@ def estimate_coefficient(self, df: pd.DataFrame) -> EffectEstimate:
         ci_high = pd.Series(bootstraps[self.bootstrap_size - bound])
 
         return EffectEstimate("coefficient", pd.Series(self.iv_coefficient(df)), ci_low, ci_high)
+
+    def to_dict(self) -> dict:
+        """
+        Convert the estimator to a python dictionary for easy serialisation as JSON or CSV.
+
+        :returns: A JSON serialisable dict representing the estimator.
+        """
+        return super().to_dict() | {"instrument": self.instrument, "bootstrap_size": self.bootstrap_size}
diff --git a/causal_testing/estimation/ipcw_estimator.py b/causal_testing/estimation/ipcw_estimator.py
index ea96c5f9..71f11769 100644
--- a/causal_testing/estimation/ipcw_estimator.py
+++ b/causal_testing/estimation/ipcw_estimator.py
@@ -11,8 +11,6 @@
 
 from causal_testing.estimation.abstract_estimator import Estimator
 from causal_testing.estimation.effect_estimate import EffectEstimate
-from causal_testing.specification.variable import Variable
-from causal_testing.testing.base_test_case import BaseTestCase
 
 logger = logging.getLogger(__name__)
 
@@ -45,7 +43,7 @@ def __init__(
         timesteps_per_observation: int,
         control_strategy: list[tuple[int, str, Any]],
         treatment_strategy: list[tuple[int, str, Any]],
-        outcome: Variable,
+        outcome_variable: str,
         status_column: str,
         fit_bl_switch_formula: str,
         fit_bltd_switch_formula: str,
@@ -54,11 +52,10 @@ def __init__(
         total_time: float = None,
     ):
         super().__init__(
-            base_test_case=BaseTestCase(None, outcome),
+            outcome_variable=outcome_variable,
+            treatment_variable=[var for _, var, _ in treatment_strategy],
             treatment_value=[val for _, _, val in treatment_strategy],
             control_value=[val for _, _, val in control_strategy],
-            adjustment_set=None,
-            effect_modifiers=None,
             alpha=alpha,
         )
         self.timesteps_per_observation = timesteps_per_observation
diff --git a/causal_testing/estimation/linear_regression_estimator.py b/causal_testing/estimation/linear_regression_estimator.py
index daa8a300..deba16ce 100644
--- a/causal_testing/estimation/linear_regression_estimator.py
+++ b/causal_testing/estimation/linear_regression_estimator.py
@@ -33,7 +33,7 @@ def gp_formula(
         seed: int = 0,
     ):
         # pylint: disable=too-many-arguments
-        """
+        r"""
         Use Genetic Programming (GP) to infer the regression equation from the data.
 
         :param df: The data to use.
@@ -52,8 +52,8 @@ def gp_formula(
         """
         gp = GP(
             df=df,
-            features=sorted(list(self.adjustment_set.union([self.base_test_case.treatment_variable.name]))),
-            outcome=self.base_test_case.outcome_variable.name,
+            features=sorted(list(self.adjustment_set.union([self.treatment_variable]))),
+            outcome=self.outcome_variable,
             extra_operators=extra_operators,
             sympy_conversions=sympy_conversions,
             seed=seed,
@@ -61,7 +61,7 @@ def gp_formula(
         )
         formula = gp.run_gp(ngen=ngen, pop_size=pop_size, num_offspring=num_offspring, seeds=seeds)
         formula = gp.simplify(formula)
-        self.formula = f"{self.base_test_case.outcome_variable.name} ~ I({formula}) - 1"
+        self.formula = f"{self.outcome_variable} ~ I({formula}) - 1"
         self._setup_covariates(df)
 
     def estimate_coefficient(self, df: pd.DataFrame) -> EffectEstimate:
@@ -73,7 +73,7 @@ def estimate_coefficient(self, df: pd.DataFrame) -> EffectEstimate:
         """
         model = self.fit_model(df)
         newline = "\n"
-        patsy_md = ModelDesc.from_formula(self.base_test_case.treatment_variable.name)
+        patsy_md = ModelDesc.from_formula(self.treatment_variable)
 
         if any(
             (
@@ -84,20 +84,15 @@ def estimate_coefficient(self, df: pd.DataFrame) -> EffectEstimate:
             )
         ):
             design_info = dmatrix(self.formula.split("~")[1], df).design_info
-            treatment = design_info.column_names[
-                design_info.term_name_slices[self.base_test_case.treatment_variable.name]
-            ]
+            treatment = design_info.column_names[design_info.term_name_slices[self.treatment_variable]]
         else:
-            treatment = [self.base_test_case.treatment_variable.name]
+            treatment = [self.treatment_variable]
         assert set(treatment).issubset(
             model.params.index.tolist()
         ), f"{treatment} not in\n{'  ' + str(model.params.index).replace(newline, newline + '  ')}"
         unit_effect = model.params[treatment]  # Unit effect is the coefficient of the treatment
         [ci_low, ci_high] = self._get_confidence_intervals(model, treatment)
 
-        if len(unit_effect) == 0:
-            unit_effect = pd.Series({self.base_test_case.treatment_variable.name: None})
-
         return EffectEstimate("coefficient", unit_effect, ci_low, ci_high)
 
     def estimate_ate(self, df: pd.DataFrame) -> EffectEstimate:
@@ -117,8 +112,8 @@ def estimate_ate(self, df: pd.DataFrame) -> EffectEstimate:
 
         # It is ABSOLUTELY CRITICAL that these go last, otherwise we can't index
         # the effect with "ate = t_test_results.effect[0]"
-        individuals.loc["control", [self.base_test_case.treatment_variable.name]] = self.control_value
-        individuals.loc["treated", [self.base_test_case.treatment_variable.name]] = self.treatment_value
+        individuals.loc["control", [self.treatment_variable]] = self.control_value
+        individuals.loc["treated", [self.treatment_variable]] = self.treatment_value
 
         # Perform a t-test to compare the predicted outcome of the control and treated individual (ATE)
         t_test_results = model.t_test(individuals.loc["treated"] - individuals.loc["control"])
diff --git a/causal_testing/specification/causal_dag.py b/causal_testing/specification/causal_dag.py
index 35b9c84b..444d74ad 100644
--- a/causal_testing/specification/causal_dag.py
+++ b/causal_testing/specification/causal_dag.py
@@ -3,14 +3,20 @@
 from __future__ import annotations
 
 import logging
+from functools import partial
 from itertools import combinations
+from multiprocessing import Pool
 from typing import Generator, Set, Union
 
 import networkx as nx
+import pandas as pd
 
-from causal_testing.testing.base_test_case import BaseTestCase
-
-from .variable import Variable
+from causal_testing.estimation.abstract_estimator import Estimator
+from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
+from causal_testing.estimation.logistic_regression_estimator import LogisticRegressionEstimator
+from causal_testing.estimation.multinomial_regression_estimator import MultinomialRegressionEstimator
+from causal_testing.testing.causal_effect import NoEffect, SomeEffect
+from causal_testing.testing.causal_test_case import CausalTestCase
 
 Node = Union[str, int]  # Node type hint: A node is a string or an int
 
@@ -123,9 +129,10 @@ class CausalDAG(nx.DiGraph):
     ensures it is acyclic. A CausalDAG must be specified as a dot file.
     """
 
-    def __init__(self, file_path: str = None, ignore_cycles: bool = False, **attr):
+    def __init__(self, file_path: str = None, ignore_cycles: bool = False, datatypes: pd.Series = None, **attr):
         super().__init__(**attr)
         self.ignore_cycles = ignore_cycles
+        self.datatypes = datatypes
         if file_path:
             if file_path.endswith(".dot"):
                 graph = nx.DiGraph(nx.nx_pydot.read_dot(file_path))
@@ -489,36 +496,50 @@ def get_backdoor_graph(self, treatments: list[str]) -> CausalDAG:
         backdoor_graph.add_edges_from(filter(lambda x: x not in outgoing_edges, self.edges))
         return backdoor_graph
 
-    def identification(self, base_test_case: BaseTestCase, avoid_variables: set[Variable] = None):
+    def identification(
+        self,
+        treatment_variable: str,
+        outcome_variable: str,
+        effect_type: str = "direct",
+        nodes_to_ignore: set[str] = None,
+    ):
         """Identify and return the minimum adjustment set
 
-        :param base_test_case: A base test case instance containing the outcome_variable and the
-                               treatment_variable required for identification.
-        :param avoid_variables: Variables not to be adjusted for (e.g. hidden variables).
+        :param treatment_variable: The treatment variable.
+        :param outcome_variable: The outcome variable.
+        :param effect_type: The type of effect (total or direct).
+        :param nodes_to_ignore: Variables not to be adjusted for (e.g. hidden variables).
         :return: The smallest set of variables which can be adjusted for to obtain a causal
                  estimate as opposed to a purely associational estimate.
         """
-        # Naive method to guarantee termination when we have cycles
+        nodes_to_ignore = set(nodes_to_ignore) if nodes_to_ignore is not None else set()
+
         if self.ignore_cycles:
-            return set(self.predecessors(base_test_case.treatment_variable.name))
-        minimal_adjustment_sets = []
-        if base_test_case.effect == "total":
-            minimal_adjustment_sets = self.enumerate_minimal_adjustment_sets(
-                [base_test_case.treatment_variable.name], [base_test_case.outcome_variable.name]
-            )
-        elif base_test_case.effect == "direct":
+            # Naive method to guarantee termination when we have cycles
+            minimal_adjustment_sets = [set(self.predecessors(treatment_variable))]
+        elif effect_type == "total":
+            minimal_adjustment_sets = self.enumerate_minimal_adjustment_sets([treatment_variable], [outcome_variable])
+        elif effect_type == "direct":
             minimal_adjustment_sets = self.direct_effect_adjustment_sets(
-                [base_test_case.treatment_variable.name], [base_test_case.outcome_variable.name]
+                [treatment_variable],
+                [outcome_variable],
+                nodes_to_ignore=nodes_to_ignore,
             )
         else:
-            raise ValueError("Causal effect should be 'total' or 'direct'")
+            raise ValueError(f"Causal effect should be 'total' or 'direct', not '{effect_type}'")
 
-        if avoid_variables is not None:
+        if nodes_to_ignore is not None:
             minimal_adjustment_sets = [
-                adj for adj in minimal_adjustment_sets if not {x.name for x in avoid_variables}.intersection(adj)
+                adj for adj in minimal_adjustment_sets if not set(nodes_to_ignore).intersection(adj)
             ]
 
-        minimal_adjustment_set = min(minimal_adjustment_sets, key=len, default=set())
+        if not minimal_adjustment_sets:
+            raise ValueError(
+                f"Could not find a suitable adjustment set for the {effect_type} effect of {treatment_variable} on "
+                f"{outcome_variable} while avoiding nodes in set {nodes_to_ignore}."
+            )
+
+        minimal_adjustment_set = min(minimal_adjustment_sets, key=len)
         return set(minimal_adjustment_set)
 
     def to_dot_string(self) -> str:
@@ -533,3 +554,164 @@ def to_dot_string(self) -> str:
 
     def __str__(self):
         return f"Nodes: {self.nodes}\nEdges: {self.edges}"
+
+    def _estimator(
+        self, treatment_variable: str, outcome_variable: str, nodes_to_ignore: set, effect_type: str = "direct"
+    ) -> Estimator:
+        if self.datatypes is None or outcome_variable not in self.datatypes:
+            raise ValueError(f"No datatype specified for {outcome_variable}.")
+
+        min_adj_set = self.identification(
+            treatment_variable=treatment_variable,
+            outcome_variable=outcome_variable,
+            nodes_to_ignore=nodes_to_ignore,
+            effect_type=effect_type,
+        )
+
+        if pd.api.types.is_bool_dtype(self.datatypes[outcome_variable]):
+            return (
+                LogisticRegressionEstimator(
+                    treatment_variable=treatment_variable, outcome_variable=outcome_variable, adjustment_set=min_adj_set
+                ),
+                "unit_odds_ratio",
+            )
+        if isinstance(self.datatypes[outcome_variable], pd.CategoricalDtype) or pd.api.types.is_object_dtype(
+            self.datatypes[outcome_variable]
+        ):
+            return (
+                MultinomialRegressionEstimator(
+                    treatment_variable=treatment_variable, outcome_variable=outcome_variable, adjustment_set=min_adj_set
+                ),
+                "unit_odds_ratio",
+            )
+        if pd.api.types.is_numeric_dtype(self.datatypes[outcome_variable]):
+            return (
+                LinearRegressionEstimator(
+                    treatment_variable=treatment_variable, outcome_variable=outcome_variable, adjustment_set=min_adj_set
+                ),
+                "coefficient",
+            )
+        raise ValueError(f"Invalid datatype for {outcome_variable}: {self.datatypes[outcome_variable]}")
+
+    def generate_causal_test(  # pylint: disable=R0912
+        self, u: str, v: str, nodes_to_ignore: set = None, **kwargs
+    ) -> list[CausalTestCase]:
+        """
+        Construct a metamorphic relation for a given node pair implied by the Causal DAG, or None if no such relation
+        can be constructed (e.g. because every valid adjustment set contains a node to ignore).
+
+        :param u: The treatment node.
+        :param v: The outcome node.
+        :param nodes_to_ignore: Set of nodes which will be excluded from causal tests.
+        :param kwargs: Keyword arguments to be passed through to test case generation.
+
+        :return: A list containing ShouldCause and ShouldNotCause metamorphic relations.
+        """
+
+        nodes_to_ignore = set(nodes_to_ignore) if nodes_to_ignore is not None else set()
+
+        causal_tests = []
+
+        # Create a ShouldNotCause relation for each pair of nodes that are not directly connected
+        if ((u, v) not in self.edges) and ((v, u) not in self.edges):
+            u_in_ancestors = u in nx.ancestors(self, v)
+            v_in_ancestors = v in nx.ancestors(self, u)
+
+            # Case 1: U --> ... --> V or U _||_ V
+            if u_in_ancestors or (not u_in_ancestors and not v_in_ancestors):
+                estimator, effect_measure = self._estimator(
+                    treatment_variable=u, outcome_variable=v, nodes_to_ignore=nodes_to_ignore
+                )
+                if estimator and effect_measure:
+                    causal_tests.append(
+                        CausalTestCase(
+                            name=f"{u} _||_ {v}",
+                            expected_causal_effect=NoEffect(),
+                            estimator=estimator,
+                            effect_measure=effect_measure,
+                            **kwargs,
+                        ),
+                    )
+
+            # Case 2: V --> ... --> U or U _||_ V
+            if v in nx.ancestors(self, u) or (not u_in_ancestors and not v_in_ancestors):
+                estimator, effect_measure = self._estimator(
+                    treatment_variable=v, outcome_variable=u, nodes_to_ignore=nodes_to_ignore
+                )
+                if estimator and effect_measure:
+                    causal_tests.append(
+                        CausalTestCase(
+                            name=f"{v} _||_ {u}",
+                            expected_causal_effect=NoEffect(),
+                            estimator=estimator,
+                            effect_measure=effect_measure,
+                            **kwargs,
+                        ),
+                    )
+
+        # Create a ShouldCause relation for each edge (u, v) or (v, u)
+        elif (u, v) in self.edges:
+            estimator, effect_measure = self._estimator(
+                treatment_variable=u, outcome_variable=v, nodes_to_ignore=nodes_to_ignore
+            )
+            if estimator and effect_measure:
+                causal_tests.append(
+                    CausalTestCase(
+                        name=f"{u} -> {v}",
+                        expected_causal_effect=SomeEffect(),
+                        estimator=estimator,
+                        effect_measure=effect_measure,
+                        **kwargs,
+                    ),
+                )
+        else:
+            estimator, effect_measure = self._estimator(
+                treatment_variable=v, outcome_variable=u, nodes_to_ignore=nodes_to_ignore
+            )
+            if estimator and effect_measure:
+                causal_tests.append(
+                    CausalTestCase(
+                        name=f"{v} -> {u}",
+                        expected_causal_effect=SomeEffect(),
+                        estimator=estimator,
+                        effect_measure=effect_measure,
+                        **kwargs,
+                    ),
+                )
+        return causal_tests
+
+    def generate_causal_tests(
+        self, nodes_to_ignore: set = None, threads: int = 0, nodes_to_test: set = None, **kwargs: dict
+    ) -> list[CausalTestCase]:
+        """
+        Construct a list of metamorphic relations implied by the Causal DAG.
+        This list of metamorphic relations contains a ShouldCause relation for every edge, and a ShouldNotCause
+        relation for every (minimal) conditional independence relation implied by the structure of the DAG.
+
+        :param nodes_to_ignore: Set of nodes which will be excluded from causal tests.
+        :param threads: Number of threads to use (if generating in parallel).
+        :param nodes_to_test: Set of nodes to test the relationships between (defaults to all nodes).
+        :param kwargs: Keyword arguments to be passed through to test case generation.
+
+        :return: A list containing ShouldCause and ShouldNotCause metamorphic relations.
+        """
+
+        nodes_to_ignore = set(nodes_to_ignore) if nodes_to_ignore is not None else set()
+        nodes_to_ignore = nodes_to_ignore.union(set(self.cycle_nodes()))
+
+        if nodes_to_test is None:
+            nodes_to_test = self.nodes
+
+        if threads < 2:
+            causal_tests = [
+                self.generate_causal_test(u, v, nodes_to_ignore, **kwargs)
+                for u, v in combinations(filter(lambda node: node not in nodes_to_ignore, nodes_to_test), 2)
+            ]
+        else:
+            with Pool(threads) as pool:
+                causal_tests = pool.starmap(
+                    partial(self.generate_causal_test, nodes_to_ignore=nodes_to_ignore, **kwargs),
+                    combinations(filter(lambda node: node not in nodes_to_ignore, nodes_to_test), 2),
+                )
+
+        return [item for items in causal_tests for item in items]
diff --git a/causal_testing/specification/scenario.py b/causal_testing/specification/scenario.py
deleted file mode 100644
index b229bda5..00000000
--- a/causal_testing/specification/scenario.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""This module holds the Scenario Class"""
-
-from collections.abc import Iterable
-from dataclasses import dataclass
-
-from .variable import Variable
-
-
-@dataclass
-class Scenario:
-    """A scenario defines the setting by listing the endogenous variables, their
-    datatypes, distributions, and any constraints over them. This is a common
-    practice in CI and is analogous to an investigator specifying “we are
-    interested in individuals over 40 who regularly eat cheese” or whatever. A
-    scenario, here, is not a specific test case; it just defines the population
-    of interest, in our case “runs of the model with parameters meeting the
-    constraints”. The model may have other inputs/outputs which the investigator
-    may choose to leave out. These are then exogenous variables and behave
-    accordingly.
-
-    :param {Variable} variables: The set of endogenous variables.
-    :param {str} constraints: The set of constraints relating the endogenous variables.
-    :attr variables:
-    :attr constraints:
-    """
-
-    def __init__(self, variables: Iterable[Variable], constraints: set[str] = None):
-        self.variables = {v.name: v for v in variables}
-        if constraints is not None:
-            self.constraints = set(constraints)
-        else:
-            self.constraints = set()
-
-    def hidden_variables(self) -> set[Variable]:
-        """Get the set of hidden variables
-
-        :return The variables marked as hidden.
-        :rtype: {Variable}
-        """
-        return {v for v in self.variables.values() if v.hidden}
diff --git a/causal_testing/specification/variable.py b/causal_testing/specification/variable.py
deleted file mode 100644
index 40d1a2e3..00000000
--- a/causal_testing/specification/variable.py
+++ /dev/null
@@ -1,90 +0,0 @@
-"""This module contains the Variable abstract class, as well as its concrete extensions: Input, Output and Meta."""
-
-from __future__ import annotations
-
-from abc import ABC
-from collections.abc import Callable
-from typing import TypeVar
-
-from pandas import DataFrame
-from scipy.stats._distn_infrastructure import rv_generic
-
-# Declare type variable
-T = TypeVar("T")
-
-
-class Variable(ABC):
-    """An abstract class representing causal variables.
-
-    :param str name: The name of the variable.
-    :param T datatype: The datatype of the variable.
-    :param rv_generic distribution: The expected distribution of the variable values.
-    :attr name:
-    :attr datatype:
-    :attr distribution:
-    :attr hidden:
-    """
-
-    name: str
-    datatype: T
-    distribution: rv_generic
-
-    def __init__(self, name: str, datatype: T, distribution: rv_generic = None, hidden: bool = False):
-        self.name = name
-        self.datatype = datatype
-        self.distribution = distribution
-        self.hidden = hidden
-
-    def __repr__(self):
-        return f"{self.typestring()}: {self.name}::{self.datatype.__name__}"
-
-    def typestring(self) -> str:
-        """Return the type of the Variable, e.g. INPUT, or OUTPUT. Note that
-        this is NOT the datatype (int, str, etc.).
-
-        :return: A string representing the variable Type.
-        :rtype: str
-
-        """
-        return type(self).__name__
-
-    def copy(self, name: str = None) -> Variable:
-        """Return a new instance of the Variable with the given name, or with
-        the original name if no name is supplied.
-
-        :param str name: The variable name.
-        :return: A new Variable instance.
-        :rtype: Variable
-
-        """
-        if name:
-            return self.__class__(name, self.datatype, self.distribution)
-        return self.__class__(self.name, self.datatype, self.distribution)
-
-
-class Input(Variable):
-    """An extension of the Variable class representing inputs."""
-
-
-class Output(Variable):
-    """An extension of the Variable class representing outputs."""
-
-
-class Meta(Variable):
-    """An extension of the Variable class representing metavariables. These are variables which are relevant to the
-    _causal_ structure and properties we may want to test, but are not directly related to the computational model
-    either as inputs or outputs.
-
-    :param str name: The name of the variable.
-    :param T datatype: The datatype of the variable.
-    :param Callable[[DataFrame], DataFrame] populate: Populate a given dataframe containing runtime data with the
-        metavariable values as calculated from model inputs and ouputs.
-
-    :attr populate: The populate function.
-    """
-
-    populate: Callable[[DataFrame], DataFrame]
-
-    def __init__(self, name: str, datatype: T, populate: Callable[[DataFrame], DataFrame]):
-        super().__init__(name, datatype)
-        self.populate = populate
diff --git a/causal_testing/surrogate/__init__.py b/causal_testing/surrogate/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/causal_testing/surrogate/causal_surrogate_assisted.py b/causal_testing/surrogate/causal_surrogate_assisted.py
deleted file mode 100644
index 4cfb6aba..00000000
--- a/causal_testing/surrogate/causal_surrogate_assisted.py
+++ /dev/null
@@ -1,154 +0,0 @@
-"""Module containing classes to define and run causal surrogate assisted test cases"""
-
-import logging
-from abc import ABC, abstractmethod
-from dataclasses import dataclass
-from typing import Callable
-
-import pandas as pd
-
-from causal_testing.estimation.cubic_spline_estimator import CubicSplineRegressionEstimator
-from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.scenario import Scenario
-from causal_testing.testing.base_test_case import BaseTestCase
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class SimulationResult:
-    """Data class holding the data and result metadata of a simulation"""
-
-    data: dict
-    fault: bool
-    relationship: str
-
-    def to_dataframe(self) -> pd.DataFrame:
-        """Convert the simulation result data to a pandas DataFrame"""
-        data_as_lists = {k: v if isinstance(v, list) else [v] for k, v in self.data.items()}
-        return pd.DataFrame(data_as_lists)
-
-
-class SearchAlgorithm(ABC):  # pylint: disable=too-few-public-methods
-    """Class to be inherited with the search algorithm consisting of a search function and the fitness function of the
-    space to be searched"""
-
-    @abstractmethod
-    def search(
-        self, surrogate_models: list[CubicSplineRegressionEstimator], scenario: Scenario, df: pd.DataFrame
-    ) -> list:
-        """Function which implements a search routine which searches for the optimal fitness value for the specified
-        scenario
-        :param surrogate_models: The surrogate models to be searched
-        :param scenario:  The modelling scenario
-        :param df: The data to use
-        """
-
-
-class Simulator(ABC):
-    """Class to be inherited with Simulator specific functions to start, shutdown and run the simulation with the give
-    config file"""
-
-    @abstractmethod
-    def startup(self, **kwargs):
-        """Function that when run, initialises and opens the Simulator"""
-
-    @abstractmethod
-    def shutdown(self, **kwargs):
-        """Function to safely exit and shutdown the Simulator"""
-
-    @abstractmethod
-    def run_with_config(self, configuration: dict) -> SimulationResult:
-        """Run the simulator with the given configuration and return the results in the structure of a
-        SimulationResult
-        :param configuration: The configuration required to initialise the Simulation
-        :return: Simulation results in the structure of the SimulationResult data class"""
-
-
-class CausalSurrogateAssistedTestCase:
-    """A class representing a single causal surrogate assisted test case."""
-
-    def __init__(
-        self,
-        scenario: Scenario,
-        causal_dag: CausalDAG,
-        search_algorithm: SearchAlgorithm,
-        simulator: Simulator,
-    ):
-        self.scenario = scenario
-        self.causal_dag = causal_dag
-        self.search_algorithm = search_algorithm
-        self.simulator = simulator
-
-    def execute(
-        self,
-        df: pd.DataFrame,
-        max_executions: int = 200,
-        custom_data_aggregator: Callable[[dict, dict], dict] = None,
-    ):
-        """For this specific test case, a search algorithm is used to find the most contradictory point in the input
-        space which is, therefore, most likely to indicate incorrect behaviour. This cadidate test case is run against
-        the simulator, checked for faults and the result returned.
-        :param df: An dataframe which contains data relevant to the specified scenario
-        :param max_executions: Maximum number of simulator executions before exiting the search
-        :param custom_data_aggregator:
-        :return: tuple containing SimulationResult or str, execution number and dataframe"""
-
-        for i in range(max_executions):
-            surrogate_models = self.generate_surrogates()
-            candidate_test_case, _, surrogate_model = self.search_algorithm.search(surrogate_models, self.scenario, df)
-
-            self.simulator.startup()
-            test_result = self.simulator.run_with_config(candidate_test_case)
-            test_result_df = test_result.to_dataframe()
-            self.simulator.shutdown()
-
-            if custom_data_aggregator is not None:
-                if df is not None:
-                    df = custom_data_aggregator(df, test_result.data)
-            else:
-                df = pd.concat([df, test_result_df], ignore_index=True)
-            if test_result.fault:
-                logger.info(
-                    f"Fault found between {surrogate_model.base_test_case.treatment_variable.name} causing "
-                    f"{surrogate_model.base_test_case.outcome_variable.name}. Contradiction with "
-                    f"expected {surrogate_model.expected_relationship}."
-                )
-                test_result.relationship = (
-                    f"{surrogate_model.base_test_case.treatment_variable.name} -> "
-                    f"{surrogate_model.base_test_case.outcome_variable.name} expected "
-                    f"{surrogate_model.expected_relationship}"
-                )
-                return test_result, i + 1, df
-
-        logger.info("No fault found")
-        return "No fault found", i + 1, df
-
-    def generate_surrogates(self) -> list[CubicSplineRegressionEstimator]:
-        """Generate a surrogate model for each edge of the DAG that specifies it is included in the DAG metadata.
-        :return: A list of surrogate models
-        """
-        surrogate_models = []
-
-        for u, v in self.causal_dag.edges:
-            edge_metadata = self.causal_dag.adj[u][v]
-            if "included" in edge_metadata:
-                from_var = self.scenario.variables.get(u)
-                to_var = self.scenario.variables.get(v)
-                base_test_case = BaseTestCase(from_var, to_var)
-
-                minimal_adjustment_set = self.causal_dag.identification(
-                    base_test_case, self.scenario.hidden_variables()
-                )
-
-                surrogate = CubicSplineRegressionEstimator(
-                    base_test_case,
-                    0,
-                    0,
-                    minimal_adjustment_set,
-                    4,
-                    expected_relationship=edge_metadata["expected"],
-                )
-                surrogate_models.append(surrogate)
-
-        return surrogate_models
diff --git a/causal_testing/surrogate/surrogate_search_algorithms.py b/causal_testing/surrogate/surrogate_search_algorithms.py
deleted file mode 100644
index 36371155..00000000
--- a/causal_testing/surrogate/surrogate_search_algorithms.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""Module containing implementation of search algorithm for surrogate search"""
-
-# Fitness functions are required to be iteratively defined, including all variables within.
-
-from operator import itemgetter
-
-import pandas as pd
-from pygad import GA
-
-from causal_testing.estimation.cubic_spline_estimator import CubicSplineRegressionEstimator
-from causal_testing.specification.scenario import Scenario
-from causal_testing.surrogate.causal_surrogate_assisted import SearchAlgorithm
-
-
-class GeneticSearchAlgorithm(SearchAlgorithm):
-    """Implementation of SearchAlgorithm class. Implements genetic search algorithm for surrogate models."""
-
-    def __init__(self, delta=0.05, config: dict = None) -> None:
-        super().__init__()
-
-        self.delta = delta
-        self.config = config
-        self.contradiction_functions = {
-            "positive": lambda x: -1 * x,
-            "negative": lambda x: x,
-            "no_effect": abs,
-            "some_effect": lambda x: abs(1 / x),
-        }
-
-    # pylint: disable=too-many-locals
-    def search(
-        self, surrogate_models: list[CubicSplineRegressionEstimator], scenario: Scenario, df: pd.DataFrame
-    ) -> list:
-        solutions = []
-
-        for surrogate_model in surrogate_models:
-            contradiction_function = self.contradiction_functions[surrogate_model.expected_relationship]
-
-            # The GA fitness function after including required variables into the function's scope
-            # Unused arguments are required for pygad's fitness function signature
-            # pylint: disable=cell-var-from-loop
-            def fitness_function(ga, solution, idx):  # pylint: disable=unused-argument
-                surrogate_model.control_value = solution[0] - self.delta
-                surrogate_model.base_test_case.treatment_variable.name_value = solution[0] + self.delta
-
-                adjustment_dict = {}
-                for i, adjustment in enumerate(surrogate_model.adjustment_set):
-                    adjustment_dict[adjustment] = solution[i + 1]
-
-                surrogate_model.adjustment_config = adjustment_dict
-                ate = surrogate_model.estimate_ate_calculated(df).value
-                if len(ate) > 1:
-                    raise ValueError(
-                        "Multiple ate values provided but currently only single values supported in this method"
-                    )
-                return contradiction_function(ate[0])
-
-            gene_types, gene_space = self.create_gene_types(surrogate_model, scenario)
-
-            num_genes = 1 + len(surrogate_model.adjustment_set)
-
-            ga = GA(
-                num_generations=200,
-                num_parents_mating=4,
-                fitness_func=fitness_function,
-                sol_per_pop=10,
-                num_genes=num_genes,
-                gene_space=gene_space,
-                gene_type=gene_types,
-                mutation_percent_genes=(1 / num_genes) * 100,
-            )
-            if self.config is not None:
-                for k, v in self.config.items():
-                    if k == "gene_space":
-                        raise ValueError(
-                            "Gene space should not be set through config. This is generated from the modelling scenario"
-                        )
-                    setattr(ga, k, v)
-
-            ga.run()
-            solution, fitness, _ = ga.best_solution()
-
-            solution_dict = {}
-            solution_dict[surrogate_model.base_test_case.treatment_variable.name] = solution[0]
-            for idx, adj in enumerate(surrogate_model.adjustment_set):
-                solution_dict[adj] = solution[idx + 1]
-            solutions.append((solution_dict, fitness, surrogate_model))
-
-        return max(solutions, key=itemgetter(1))  # This can be done better with fitness normalisation between edges
-
-    @staticmethod
-    def create_gene_types(surrogate_model: CubicSplineRegressionEstimator, scenario: Scenario) -> tuple[list, list]:
-        """Generate the gene_types and gene_space for a given fitness function and scenario
-        :param surrogate_model: Instance of a CubicSplineRegressionEstimator
-        :param scenario: The modelling scenario"""
-
-        var_space = {}
-        var_space[surrogate_model.base_test_case.treatment_variable.name] = {}
-        for adj in surrogate_model.adjustment_set:
-            var_space[adj] = {}
-
-        for relationship in list(scenario.constraints):
-            rel_split = str(relationship).split(" ")
-
-            if rel_split[0] in var_space:
-                datatype = scenario.variables.get(rel_split[0]).datatype
-                if rel_split[1] == ">=":
-                    var_space[rel_split[0]]["low"] = datatype(rel_split[2])
-                elif rel_split[1] == "<=":
-                    if datatype == int:
-                        var_space[rel_split[0]]["high"] = int(rel_split[2]) + 1
-                    else:
-                        var_space[rel_split[0]]["high"] = datatype(rel_split[2])
-        gene_space = []
-        gene_space.append(var_space[surrogate_model.base_test_case.treatment_variable.name])
-        for adj in surrogate_model.adjustment_set:
-            gene_space.append(var_space[adj])
-
-        gene_types = []
-        gene_types.append(scenario.variables.get(surrogate_model.base_test_case.treatment_variable.name).datatype)
-        for adj in surrogate_model.adjustment_set:
-            gene_types.append(scenario.variables.get(adj).datatype)
-        return gene_types, gene_space
diff --git a/causal_testing/testing/base_test_case.py b/causal_testing/testing/base_test_case.py
deleted file mode 100644
index aca1b376..00000000
--- a/causal_testing/testing/base_test_case.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""This module contains the BaseTestCase dataclass, which stores the information required for identification"""
-
-from dataclasses import dataclass
-
-from causal_testing.specification.variable import Variable
-from causal_testing.testing.effect import Effect
-
-
-@dataclass(frozen=True)
-class BaseTestCase:
-    """
-    A base causal test case represents the relationship of an edge on a causal DAG.
-    :param treatment_variable: A causal variable representing the treatment/control variable
-    :param outcome_variable: A causal variable representing the outcome/output variable
-    :param effect: A string representing the effect, current support effects are 'direct' and 'total'
-    """
-
-    treatment_variable: Variable
-    outcome_variable: Variable
-    effect: str = Effect.TOTAL.value
-
-    def __post_init__(self):
-        if self.treatment_variable == self.outcome_variable:
-            raise ValueError(f"Treatment variable {self.treatment_variable} cannot also be the outcome variable.")
diff --git a/causal_testing/testing/causal_effect.py b/causal_testing/testing/causal_effect.py
index efa34732..a99a3bd1 100644
--- a/causal_testing/testing/causal_effect.py
+++ b/causal_testing/testing/causal_effect.py
@@ -3,45 +3,49 @@
 ExactValue, Positive, Negative, SomeEffect, NoEffect"""
 
 from abc import ABC, abstractmethod
-from collections.abc import Iterable
 
 import numpy as np
 
-from causal_testing.testing.causal_test_result import CausalTestResult
+from causal_testing.estimation.effect_estimate import EffectEstimate
 
 
 class CausalEffect(ABC):
     """An abstract class representing an expected causal effect."""
 
+    def __init__(self, effect_type: str = "direct"):
+        self.effect_type = effect_type
+
     @abstractmethod
-    def apply(self, res: CausalTestResult) -> bool:
+    def apply(self, effect_estimate: EffectEstimate) -> bool:
         """Abstract apply method that should return a bool representing if the result meets the outcome
-        :param res: CausalTestResult to be checked
+        :param effect_estimate: EffectEstimate to be checked
         :return: Bool that is true if outcome is met
         """
 
     def __str__(self) -> str:
         return type(self).__name__
 
+    def to_dict(self):
+        """
+        Convert the expected effect to a python dictionary for easy serialisation as JSON.
+
+        :returns: A JSON serialisable dict representing the expected effect.
+        """
+        return {"name": self.__class__.__name__, "effect_type": self.effect_type}
+
 
 class SomeEffect(CausalEffect):
     """An extension of CausalEffect representing that the expected causal effect should not be zero."""
 
-    def apply(self, res: CausalTestResult) -> bool:
-        if res.effect_estimate.ci_low is None or res.effect_estimate.ci_high is None:
-            return None
-        if res.effect_estimate.type in ("risk_ratio", "hazard_ratio", "unit_odds_ratio"):
-            return any(
-                1 < ci_low < ci_high or ci_low < ci_high < 1
-                for ci_low, ci_high in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high)
-            )
-        if res.effect_estimate.type in ("coefficient", "ate"):
-            return any(
-                0 < ci_low < ci_high or ci_low < ci_high < 0
-                for ci_low, ci_high in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high)
-            )
+    def apply(self, effect_estimate: EffectEstimate) -> bool:
+        if effect_estimate.type in ("risk_ratio", "hazard_ratio", "unit_odds_ratio", "odds_ratio"):
+            value_to_check = 1
+        elif effect_estimate.type in ("coefficient", "ate"):
+            value_to_check = 0
+        else:
+            raise ValueError(f"Test Value type {effect_estimate.type} is not valid for this CausalEffect")
 
-        raise ValueError(f"Test Value type {res.effect_estimate.type} is not valid for this CausalEffect")
+        return (~((effect_estimate.ci_low <= value_to_check) & (value_to_check <= effect_estimate.ci_high))).all()
 
 
 class NoEffect(CausalEffect):
@@ -52,40 +56,40 @@ class NoEffect(CausalEffect):
     :param ctol: Categorical tolerance. The test will pass if this proportion of categories pass.
     """
 
-    def __init__(self, atol: float = 1e-10, ctol: float = 0.05):
+    def __init__(self, effect_type: str = "direct", atol: float = 0, ctol: float = 0.0):
+        super().__init__(effect_type=effect_type)
         self.atol = atol
         self.ctol = ctol
 
-    def apply(self, res: CausalTestResult) -> bool:
-        if res.effect_estimate.type in ("risk_ratio", "hazard_ratio", "unit_odds_ratio", "odds_ratio"):
-            return any(
-                ci_low < 1 < ci_high or np.isclose(value, 1.0, atol=self.atol)
-                for ci_low, ci_high, value in zip(
-                    res.effect_estimate.ci_low, res.effect_estimate.ci_high, res.effect_estimate.value
-                )
-            )
-        if res.effect_estimate.type in ("coefficient", "ate"):
-            value = (
-                res.effect_estimate.value
-                if isinstance(res.effect_estimate.ci_high, Iterable)
-                else [res.effect_estimate.value]
-            )
-            return (
-                sum(
-                    not ((ci_low < 0 < ci_high) or abs(v) < self.atol)
-                    for ci_low, ci_high, v in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high, value)
-                )
-                / len(value)
-                < self.ctol
-            )
+    def apply(self, effect_estimate: EffectEstimate) -> bool:
+        if effect_estimate.type in ("risk_ratio", "hazard_ratio", "unit_odds_ratio", "odds_ratio"):
+            value_to_check = 1
+        elif effect_estimate.type in ("coefficient", "ate"):
+            value_to_check = 0
+        else:
+            raise ValueError(f"Test Value type {effect_estimate.type} is not valid for this CausalEffect")
+
+        return sum(
+            ((effect_estimate.ci_low <= value_to_check) & (value_to_check <= effect_estimate.ci_high))
+            | (np.isclose(effect_estimate.value, value_to_check, atol=self.atol))
+        ) / len(effect_estimate.value) >= (1 - self.ctol)
 
-        raise ValueError(f"Test Value type {res.effect_estimate.type} is not valid for this CausalEffect")
+    def to_dict(self):
+        """
+        Convert the expected effect to a python dictionary for easy serialisation as JSON.
+
+        :returns: A JSON serialisable dict representing the expected effect.
+        """
+        return super().to_dict() | {"atol": self.atol, "ctol": self.ctol}
 
 
 class ExactValue(CausalEffect):
     """An extension of CausalEffect representing that the expected causal effect should be a specific value."""
 
-    def __init__(self, value: float, atol: float = None, ci_low: float = None, ci_high: float = None):
+    def __init__(
+        self, value: float, effect_type: str = "direct", atol: float = 0, ci_low: float = None, ci_high: float = None
+    ):
+        super().__init__(effect_type=effect_type)
         if (ci_low is not None) ^ (ci_high is not None):
             raise ValueError("If specifying confidence intervals, must specify `ci_low` and `ci_high` parameters.")
         if atol is not None and atol < 0:
@@ -94,7 +98,7 @@ def __init__(self, value: float, atol: float = None, ci_low: float = None, ci_hi
         self.value = value
         self.ci_low = ci_low
         self.ci_high = ci_high
-        self.atol = atol if atol is not None else abs(value * 0.05)
+        self.atol = atol
 
         if self.ci_low is not None and self.ci_high is not None:
             if not self.ci_low <= self.value <= self.ci_high:
@@ -102,54 +106,61 @@ def __init__(self, value: float, atol: float = None, ci_low: float = None, ci_hi
             if self.value - self.atol < self.ci_low or self.value + self.atol > self.ci_high:
                 raise ValueError(
                     "Arithmetic tolerance falls outside the confidence intervals."
-                    "Try specifying a smaller value of atol."
+                    f"Try specifying wider intervals or a value of atol smaller than the current vlaue {self.atol}."
                 )
 
-    def apply(self, res: CausalTestResult) -> bool:
-        close = np.isclose(res.effect_estimate.value, self.value, atol=self.atol)
-        if res.effect_estimate.ci_valid and self.ci_low is not None and self.ci_high is not None:
-            return all(
-                close and self.ci_low <= ci_low and self.ci_high >= ci_high
-                for ci_low, ci_high in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high)
+    def apply(self, effect_estimate: EffectEstimate) -> bool:
+        close = np.isclose(effect_estimate.value, self.value, atol=self.atol)
+        if effect_estimate.ci_valid and self.ci_low is not None and self.ci_high is not None:
+            return (
+                close.all()
+                and (self.ci_low <= effect_estimate.ci_low).all()
+                and (self.ci_high >= effect_estimate.ci_high).all()
             )
-        return close
+        return close.all()
 
     def __str__(self):
         return f"ExactValue: {self.value}±{self.atol}"
 
+    def to_dict(self):
+        """
+        Convert the expected effect to a python dictionary for easy serialisation as JSON or CSV.
+
+        :returns: A JSON serialisable dict representing the expected effect.
+        """
+        effect = {"value": self.value, "atol": self.atol}
+        if self.ci_low:
+            effect["ci_low"] = self.ci_low
+        if self.ci_low:
+            effect["ci_high"] = self.ci_high
+
+        return super().to_dict() | effect
+
 
 class Positive(SomeEffect):
     """An extension of CausalEffect representing that the expected causal effect should be positive.
     Currently only single values are supported for the test value"""
 
-    def apply(self, res: CausalTestResult) -> bool:
-        if len(res.effect_estimate.value) > 1:
+    def apply(self, effect_estimate: EffectEstimate) -> bool:
+        if len(effect_estimate.value) > 1:
             raise ValueError("Positive Effects are currently only supported on single float datatypes")
-        if res.effect_estimate.type in {"ate", "coefficient"}:
-            return any(
-                0 < ci_low < ci_high for ci_low, ci_high in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high)
-            )
-        if res.effect_estimate.type in ["risk_ratio", "unit_odds_ratio"]:
-            return any(
-                1 < ci_low < ci_high for ci_low, ci_high in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high)
-            )
-        raise ValueError(f"Test Value type {res.effect_estimate.type} is not valid for this CausalEffect")
+        if effect_estimate.type in {"ate", "coefficient"}:
+            return any(0 < ci_low < ci_high for ci_low, ci_high in zip(effect_estimate.ci_low, effect_estimate.ci_high))
+        if effect_estimate.type in ["risk_ratio", "unit_odds_ratio"]:
+            return any(1 < ci_low < ci_high for ci_low, ci_high in zip(effect_estimate.ci_low, effect_estimate.ci_high))
+        raise ValueError(f"Test Value type {effect_estimate.type} is not valid for this CausalEffect")
 
 
 class Negative(SomeEffect):
     """An extension of CausalEffect representing that the expected causal effect should be negative.
     Currently only single values are supported for the test value"""
 
-    def apply(self, res: CausalTestResult) -> bool:
-        if len(res.effect_estimate.value) > 1:
+    def apply(self, effect_estimate: EffectEstimate) -> bool:
+        if len(effect_estimate.value) > 1:
             raise ValueError("Negative Effects are currently only supported on single float datatypes")
-        if res.effect_estimate.type in {"ate", "coefficient"}:
-            return any(
-                ci_low < ci_high < 0 for ci_low, ci_high in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high)
-            )
-        if res.effect_estimate.type in ["risk_ratio", "unit_odds_ratio"]:
-            return any(
-                ci_low < ci_high < 1 for ci_low, ci_high in zip(res.effect_estimate.ci_low, res.effect_estimate.ci_high)
-            )
+        if effect_estimate.type in {"ate", "coefficient"}:
+            return any(ci_low < ci_high < 0 for ci_low, ci_high in zip(effect_estimate.ci_low, effect_estimate.ci_high))
+        if effect_estimate.type in ["risk_ratio", "unit_odds_ratio"]:
+            return any(ci_low < ci_high < 1 for ci_low, ci_high in zip(effect_estimate.ci_low, effect_estimate.ci_high))
         # Dead code but necessary for pylint
-        raise ValueError(f"Test Value type {res.effect_estimate.type} is not valid for this CausalEffect")
+        raise ValueError(f"Test Value type {effect_estimate.type} is not valid for this CausalEffect")
diff --git a/causal_testing/testing/causal_test_case.py b/causal_testing/testing/causal_test_case.py
index 8b32df32..fa6e4c90 100644
--- a/causal_testing/testing/causal_test_case.py
+++ b/causal_testing/testing/causal_test_case.py
@@ -6,9 +6,8 @@
 import pandas as pd
 
 from causal_testing.estimation.abstract_estimator import Estimator
-from causal_testing.testing.base_test_case import BaseTestCase
 from causal_testing.testing.causal_effect import CausalEffect
-from causal_testing.testing.causal_test_result import CausalTestResult
+from causal_testing.testing.causal_test_result import CausalTestResult, TestOutcome
 from causal_testing.testing.data_adequacy import DataAdequacy
 
 logger = logging.getLogger(__name__)
@@ -23,33 +22,46 @@ class CausalTestCase:
     causes the model-under-test to produce the expected change.
     :param base_test_case: A BaseTestCase object consisting of a treatment variable, outcome variable and effect
     :param expected_causal_effect: The expected causal effect (Positive, Negative, No Effect).
-    :param estimate_type: A string which denotes the type of estimate to return.
+    :param effect_measure: A string which denotes the type of estimate to return.
     :param estimator: An Estimator class object
     """
 
     def __init__(
         # pylint: disable=too-many-arguments
         self,
-        base_test_case: BaseTestCase,
         expected_causal_effect: CausalEffect,
-        estimate_type: str = "ate",
+        effect_measure: str,
         estimator: type(Estimator) = None,
         name: str = None,
         query: str = None,
         skip: bool = False,
     ):
-        self.base_test_case = base_test_case
         self.expected_causal_effect = expected_causal_effect
-        self.outcome_variable = base_test_case.outcome_variable
-        self.treatment_variable = base_test_case.treatment_variable
-        self.estimate_type = estimate_type
+        self.effect_measure = effect_measure
         self.estimator = estimator
-        self.effect = base_test_case.effect
         self.result = None
         self.name = name
         self.query = query
         self.skip = skip
 
+    @property
+    def treatment_variable(self):
+        """
+        :returns: The treatment variable of the test case.
+        """
+        if self.estimator is not None:
+            return self.estimator.treatment_variable
+        return None
+
+    @property
+    def outcome_variable(self):
+        """
+        :returns: The outcome variable of the test case.
+        """
+        if self.estimator is not None:
+            return self.estimator.outcome_variable
+        return None
+
     def measure_adequacy(
         self,
         df: pd.DataFrame,
@@ -69,34 +81,42 @@ def measure_adequacy(
             if group_by is not None:
                 ids = pd.Series(df[group_by].unique())
                 ids = ids.sample(len(ids), replace=True, random_state=i)
-                df = df[df[group_by].isin(ids)]
+                sample_df = df[df[group_by].isin(ids)]
             else:
-                df = df.sample(len(df), replace=True, random_state=i)
+                sample_df = df.sample(len(df), replace=True, random_state=i)
             try:
-                result = self.estimate_effect(df)
-                outcomes.append(self.expected_causal_effect.apply(result))
-                results.append(result.effect_estimate.to_df())
-            # Could get a variety of exceptions here due to insufficient/badly formed data
+                effect_estimate = self.estimate_effect(sample_df)
+                passed = self.expected_causal_effect.apply(effect_estimate=effect_estimate)
+                outcomes.append(passed)
+                results.append(effect_estimate.to_df().assign(test_index=i, passed=passed))
+            # Could get a variety of exceptions here due to insufficient/badly formed data in the sample
             # We don't want these to stop execution
             except Exception:  # pylint: disable=W0718
-                pass
+                outcomes.append(None)
 
-        results = pd.concat(results)
+        if results:
+            results = pd.concat(results)
 
-        results["var"] = results.index
-        results["passed"] = outcomes
+            results["var"] = results.index
 
+            return DataAdequacy(
+                results=results,
+                kurtosis=results.groupby("var")["effect_estimate"].apply(lambda x: x.kurtosis()),
+                passing=int(sum(filter(lambda x: x is not None, outcomes))),
+                successful=int(sum(x is not None for x in outcomes)),
+                bootstrap_size=bootstrap_size,
+            )
         return DataAdequacy(
             results=results,
-            kurtosis=results.groupby("var")["effect_estimate"].apply(lambda x: x.kurtosis()),
-            passing=sum(filter(lambda x: x is not None, outcomes)),
-            successful=sum(x is not None for x in outcomes),
+            kurtosis=None,
+            passing=int(sum(filter(lambda x: x is not None, outcomes))),
+            successful=int(sum(x is not None for x in outcomes)),
+            bootstrap_size=bootstrap_size,
         )
 
     def execute_test(
         self,
         df: pd.DataFrame,
-        estimate_params: dict[str, any] = None,
         adequacy: bool = False,
         suppress_estimation_errors: bool = False,
         bootstrap_size: int = 100,
@@ -106,7 +126,6 @@ def execute_test(
         Execute a causal test case.
 
         :param df: The data to use.
-        :param estimate_params: Extra parameters for the estimate calculation.
         :param adequacy: Set to True to calculate the causal test adequacy associated with the effect estimate.
         :param suppress_estimation_errors: Set to True to suppress estimation errors. (Defaults to False)
         :param bootstrap_size: The number of bootstrap samples to use. (Defaults to 100)
@@ -115,46 +134,61 @@ def execute_test(
         :return causal_test_result: A CausalTestResult for the executed causal test case.
         """
         if not self.skip:
-            self.result = self.estimate_effect(
-                df=df, estimate_params=estimate_params, suppress_estimation_errors=suppress_estimation_errors
-            )
-            if adequacy:
-                self.result.adequacy = self.measure_adequacy(df=df, bootstrap_size=bootstrap_size, group_by=group_by)
-
-    def estimate_effect(
-        self,
-        df: pd.DataFrame,
-        estimate_params: dict[str, any] = None,
-        suppress_estimation_errors: bool = False,
-    ) -> CausalTestResult:
+            try:
+                effect_estimate = self.estimate_effect(df=df)
+                self.result = CausalTestResult(
+                    effect_estimate=effect_estimate,
+                    outcome=(
+                        TestOutcome.PASS
+                        if self.expected_causal_effect.apply(effect_estimate=effect_estimate)
+                        else TestOutcome.FAIL
+                    ),
+                    adequacy=(
+                        self.measure_adequacy(df=df, bootstrap_size=bootstrap_size, group_by=group_by)
+                        if adequacy
+                        else None
+                    ),
+                )
+            except (np.linalg.LinAlgError, ValueError) as e:
+                if not suppress_estimation_errors:
+                    raise e
+                self.result = CausalTestResult(
+                    effect_estimate=None, outcome=TestOutcome.INESTIMABLE, error_message=str(e)
+                )
+
+    def estimate_effect(self, df: pd.DataFrame) -> CausalTestResult:
         """
         Execute a causal test case and return the causal test result.
 
         :param df: The data to use.
-        :param estimate_params: Extra parameters for the estimate calculation.
-        :param suppress_estimation_errors: Set to True to suppress estimation errors. (Defaults to False)
         :return causal_test_result: A CausalTestResult for the executed causal test case.
         """
         if self.query:
             df = df.query(self.query)
-        if not hasattr(self.estimator, f"estimate_{self.estimate_type}"):
-            raise AttributeError(f"{self.estimator.__class__} has no {self.estimate_type} method.")
-        estimate_effect = getattr(self.estimator, f"estimate_{self.estimate_type}")
-        try:
-            effect_estimate = estimate_effect(df, **(estimate_params if estimate_params is not None else {}))
-            return CausalTestResult(
-                effect_estimate=effect_estimate,
-            )
-        except (np.linalg.LinAlgError, ValueError) as e:
-            if not suppress_estimation_errors:
-                raise e
-            return CausalTestResult(effect_estimate=None, error_message=str(e))
-
-    def __str__(self):
-        treatment_config = {self.treatment_variable.name: self.estimator.treatment_value}
-        control_config = {self.treatment_variable.name: self.estimator.control_value}
-        outcome_variable = {self.outcome_variable.name}
-        return (
-            f"Running {treatment_config} instead of {control_config} should cause the following "
-            f"changes to {outcome_variable}: {self.expected_causal_effect}."
-        )
+        if not hasattr(self.estimator, f"estimate_{self.effect_measure}"):
+            raise AttributeError(f"{self.estimator.__class__} has no {self.effect_measure} method.")
+        estimate_effect = getattr(self.estimator, f"estimate_{self.effect_measure}")
+        return estimate_effect(df)
+
+    def to_dict(self) -> dict:
+        """
+        Convert the test case to a python dictionary for easy serialisation as JSON.
+
+        :returns: A JSON serialisable dict representing the test case.
+        """
+        test_case = {
+            "name": self.name,
+            "skip": self.skip,
+            "effect_measure": self.effect_measure,
+            "query": self.query,
+        }
+
+        for label, attribute in [
+            ("expected_effect", self.expected_causal_effect),
+            ("estimator", self.estimator),
+            ("result", self.result),
+        ]:
+            if attribute is not None:
+                test_case[label] = attribute.to_dict()
+
+        return test_case
diff --git a/causal_testing/testing/causal_test_result.py b/causal_testing/testing/causal_test_result.py
index 79d17506..5bfb15c1 100644
--- a/causal_testing/testing/causal_test_result.py
+++ b/causal_testing/testing/causal_test_result.py
@@ -1,9 +1,12 @@
 """This module contains the CausalTestResult class, which is a container for the results of a causal test."""
 
 from dataclasses import dataclass
+from enum import Enum
 
 from causal_testing.estimation.effect_estimate import EffectEstimate
 
+TestOutcome = Enum("TestOutcome", [("PASS", 2), ("FAIL", 0), ("INESTIMABLE", 1)])
+
 
 @dataclass
 class CausalTestResult:
@@ -14,9 +17,36 @@ class CausalTestResult:
     def __init__(
         self,
         effect_estimate: EffectEstimate,
+        outcome: TestOutcome,
         adequacy=None,
         error_message: str = None,
     ):
-        self.adequacy = adequacy
         self.effect_estimate = effect_estimate
+        self.outcome = outcome
+        self.adequacy = adequacy
         self.error_message = error_message
+
+    @property
+    def passed(self) -> bool:
+        """
+        Check whether the test has passed.
+        :returns: True if the test outcome is PASS.
+        """
+        return self.outcome == TestOutcome.PASS
+
+    def to_dict(self):
+        """
+        Convert the result to a python dictionary for easy serialisation as JSON.
+
+        :returns: A JSON serialisable dict representing the test result.
+        """
+
+        outcome = {"outcome": self.outcome.name, "passed": self.passed}
+        if self.error_message:
+            outcome["error_message"] = self.error_message
+
+        effect_estimate = self.effect_estimate.to_dict() if self.effect_estimate else {}
+
+        adequacy = self.adequacy.to_dict() if self.adequacy else {}
+
+        return outcome | effect_estimate | {"adequacy": adequacy}
diff --git a/causal_testing/testing/data_adequacy.py b/causal_testing/testing/data_adequacy.py
index 0f9c5105..192ef1a1 100644
--- a/causal_testing/testing/data_adequacy.py
+++ b/causal_testing/testing/data_adequacy.py
@@ -4,6 +4,8 @@
 
 import logging
 
+from pandas import Series
+
 logger = logging.getLogger(__name__)
 
 
@@ -20,21 +22,29 @@ class DataAdequacy:
     # pylint: disable=too-many-instance-attributes
     def __init__(
         self,
-        kurtosis=None,
-        passing=None,
-        results=None,
-        successful=None,
+        kurtosis: Series = None,
+        passing: int = None,
+        results: dict = None,
+        successful: int = None,
+        bootstrap_size: int = None,
     ):
         self.kurtosis = kurtosis
         self.passing = passing
         self.results = results
         self.successful = successful
+        self.bootstrap_size = bootstrap_size
 
-    def to_dict(self):
-        """Returns the adequacy object as a dictionary."""
-        return {
+    def to_dict(self, include_results: bool = False):
+        """
+        :returns: the adequacy object as a dictionary.
+        :param include_results: Whether to serialise the results.
+        """
+        result = {
             "kurtosis": self.kurtosis.to_dict(),
             "passing": self.passing,
             "successful": self.successful,
-            "results": self.results.reset_index(drop=True).to_dict(),
+            "bootstrap_size": self.bootstrap_size,
         }
+        if include_results:
+            return result | {"results": self.results.reset_index(drop=True).to_dict()}
+        return result
diff --git a/causal_testing/testing/metamorphic_relation.py b/causal_testing/testing/metamorphic_relation.py
deleted file mode 100644
index 599c6cb5..00000000
--- a/causal_testing/testing/metamorphic_relation.py
+++ /dev/null
@@ -1,305 +0,0 @@
-"""
-This module contains the ShouldCause and ShouldNotCause metamorphic relations as
-defined in our ICST paper [https://eprints.whiterose.ac.uk/195317/].
-"""
-
-import json
-import logging
-from dataclasses import dataclass
-from itertools import combinations
-from multiprocessing import Pool
-from typing import Iterable
-
-import networkx as nx
-
-from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.testing.base_test_case import BaseTestCase
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass(order=True)
-class MetamorphicRelation:
-    """Class representing a metamorphic relation."""
-
-    base_test_case: BaseTestCase
-    adjustment_vars: Iterable[str]
-
-    def __eq__(self, other):
-        same_type = self.__class__ == other.__class__
-        same_treatment = self.base_test_case.treatment_variable == other.base_test_case.treatment_variable
-        same_outcome = self.base_test_case.outcome_variable == other.base_test_case.outcome_variable
-        same_effect = self.base_test_case.effect == other.base_test_case.effect
-        same_adjustment_set = set(self.adjustment_vars) == set(other.adjustment_vars)
-        return same_type and same_treatment and same_outcome and same_effect and same_adjustment_set
-
-    def to_json_stub(
-        self,
-        skip: bool = False,
-        estimate_type: str = "coefficient",
-        effect_type: str = "direct",
-        estimator: str = "LinearRegressionEstimator",
-        alpha: float = 0.05,
-    ) -> dict:
-        """
-        Convert to a JSON frontend stub string for user customisation.
-        :param skip: Whether to skip the test (default False).
-        :param effect_type: The type of causal effect to consider (total or direct)
-        :param estimate_type: The estimate type to use when evaluating tests
-        :param estimator: The name of the estimator class to use when evaluating the test
-        :param alpha: The significance level to use when calculating the confidence intervals
-        """
-        if estimator not in [
-            "LinearRegressionEstimator",
-            "LogisticRegressionEstimator",
-            "MultinomialRegressionEstimator",
-        ]:
-            raise ValueError(
-                f"Unsupported estimator {estimator}. "
-                "We only support autogeneration using LinearRegressionEstimator or LogisticRegressionEstimator."
-                "More advanced estimators require careful thought that cannot be easily automated."
-            )
-        return {
-            "name": str(self),
-            "estimator": estimator,
-            "estimate_type": estimate_type,
-            "effect": effect_type,
-            "treatment_variable": self.base_test_case.treatment_variable,
-            "alpha": alpha,
-            "skip": skip,
-            "estimator_kwargs": {
-                "formula": (
-                    f"{self.base_test_case.outcome_variable} ~ "
-                    f"{' + '.join([self.base_test_case.treatment_variable] + self.adjustment_vars)}"
-                ),
-            },
-        }
-
-
-class ShouldCause(MetamorphicRelation):
-    """Class representing a should cause metamorphic relation."""
-
-    def to_json_stub(
-        self,
-        skip: bool = False,
-        estimate_type: str = "coefficient",
-        effect_type: str = "direct",
-        estimator: str = "LinearRegressionEstimator",
-        alpha: float = 0.05,
-    ) -> dict:
-        """
-        Convert to a JSON frontend stub string for user customisation.
-        :param skip: Whether to skip the test (default False).
-        :param effect_type: The type of causal effect to consider (total or direct)
-        :param estimate_type: The estimate type to use when evaluating tests
-        :param estimator: The name of the estimator class to use when evaluating the test
-        :param alpha: The significance level to use when calculating the confidence intervals
-        """
-        return super().to_json_stub(
-            skip=skip, estimate_type=estimate_type, effect_type=effect_type, estimator=estimator, alpha=alpha
-        ) | {
-            "expected_effect": {self.base_test_case.outcome_variable: "SomeEffect"},
-        }
-
-    def __str__(self):
-        formatted_str = f"{self.base_test_case.treatment_variable} --> {self.base_test_case.outcome_variable}"
-        if self.adjustment_vars:
-            formatted_str += f" | {self.adjustment_vars}"
-        return formatted_str
-
-
-class ShouldNotCause(MetamorphicRelation):
-    """Class representing a should cause metamorphic relation."""
-
-    def to_json_stub(
-        self,
-        skip: bool = False,
-        estimate_type: str = "coefficient",
-        effect_type: str = "direct",
-        estimator: str = "LinearRegressionEstimator",
-        alpha: float = 0.05,
-    ) -> dict:
-        """
-        Convert to a JSON frontend stub string for user customisation.
-        :param skip: Whether to skip the test (default False).
-        :param effect_type: The type of causal effect to consider (total or direct)
-        :param estimate_type: The estimate type to use when evaluating tests
-        :param estimator: The name of the estimator class to use when evaluating the test
-        :param alpha: The significance level to use when calculating the confidence intervals
-        """
-        return super().to_json_stub(
-            skip=skip, estimate_type=estimate_type, effect_type=effect_type, estimator=estimator, alpha=alpha
-        ) | {
-            "expected_effect": {self.base_test_case.outcome_variable: "NoEffect"},
-        }
-
-    def __str__(self):
-        formatted_str = f"{self.base_test_case.treatment_variable} _||_ {self.base_test_case.outcome_variable}"
-        if self.adjustment_vars:
-            formatted_str += f" | {self.adjustment_vars}"
-        return formatted_str
-
-
-def min_adj_set(adj_sets: set[set[str]]) -> set[str]:
-    """
-    Given a nonempty set of adjustment sets, return the minimal one.
-    :param adj_sets: A nonempty set of adjustment sets.
-    :return: The minimal adjustment set (by alphabetical order if there are multiple sets of the same size).
-    """
-    return sorted(list(map(lambda s: sorted(list(s)), adj_sets)))[0]
-
-
-def generate_metamorphic_relation(  # pylint: disable=R0912
-    node_pair: tuple[str, str], dag: CausalDAG, nodes_to_ignore: set = None
-) -> MetamorphicRelation:
-    """
-    Construct a metamorphic relation for a given node pair implied by the Causal DAG, or None if no such relation can
-    be constructed (e.g. because every valid adjustment set contains a node to ignore).
-
-    :param node_pair: The pair of nodes to consider.
-    :param dag: Causal DAG from which the metamorphic relations will be generated.
-    :param nodes_to_ignore: Set of nodes which will be excluded from causal tests.
-
-    :return: A list containing ShouldCause and ShouldNotCause metamorphic relations.
-    """
-
-    if nodes_to_ignore is None:
-        nodes_to_ignore = set()
-
-    (u, v) = node_pair
-    metamorphic_relations = []
-
-    # Create a ShouldNotCause relation for each pair of nodes that are not directly connected
-    if ((u, v) not in dag.edges) and ((v, u) not in dag.edges):
-        # Case 1: U --> ... --> V
-        if u in nx.ancestors(dag, v):
-            adj_sets = dag.direct_effect_adjustment_sets([u], [v], nodes_to_ignore=nodes_to_ignore)
-            if adj_sets:
-                metamorphic_relations.append(ShouldNotCause(BaseTestCase(u, v), min_adj_set(adj_sets)))
-
-        # Case 2: V --> ... --> U
-        elif v in nx.ancestors(dag, u):
-            adj_sets = dag.direct_effect_adjustment_sets([v], [u], nodes_to_ignore=nodes_to_ignore)
-            if adj_sets:
-                metamorphic_relations.append(ShouldNotCause(BaseTestCase(v, u), min_adj_set(adj_sets)))
-
-        # Case 3: V _||_ U (No directed walk from V to U but there may be a back-door path e.g. U <-- Z --> V).
-        else:
-            adj_sets1 = dag.direct_effect_adjustment_sets([u], [v], nodes_to_ignore=nodes_to_ignore)
-            adj_sets2 = dag.direct_effect_adjustment_sets([v], [u], nodes_to_ignore=nodes_to_ignore)
-            if adj_sets1:
-                metamorphic_relations.append(ShouldNotCause(BaseTestCase(u, v), list(adj_sets1[0])))
-            if adj_sets2:
-                metamorphic_relations.append(ShouldNotCause(BaseTestCase(v, u), list(adj_sets2[0])))
-
-    # Create a ShouldCause relation for each edge (u, v) or (v, u)
-    elif (u, v) in dag.edges:
-        adj_sets = dag.direct_effect_adjustment_sets([u], [v], nodes_to_ignore=nodes_to_ignore)
-        if adj_sets:
-            metamorphic_relations.append(ShouldCause(BaseTestCase(u, v), min_adj_set(adj_sets)))
-    else:
-        adj_sets = dag.direct_effect_adjustment_sets([v], [u], nodes_to_ignore=nodes_to_ignore)
-        if adj_sets:
-            metamorphic_relations.append(ShouldCause(BaseTestCase(v, u), min_adj_set(adj_sets)))
-    return metamorphic_relations
-
-
-def generate_metamorphic_relations(
-    dag: CausalDAG, nodes_to_ignore: set = None, threads: int = 0, nodes_to_test: set = None
-) -> list[MetamorphicRelation]:
-    """
-    Construct a list of metamorphic relations implied by the Causal DAG.
-    This list of metamorphic relations contains a ShouldCause relation for every edge, and a ShouldNotCause
-    relation for every (minimal) conditional independence relation implied by the structure of the DAG.
-
-    :param dag: Causal DAG from which the metamorphic relations will be generated.
-    :param nodes_to_ignore: Set of nodes which will be excluded from causal tests.
-    :param threads: Number of threads to use (if generating in parallel).
-    :param nodes_to_test: Set of nodes to test the relationships between (defaults to all nodes).
-
-    :return: A list containing ShouldCause and ShouldNotCause metamorphic relations.
-    """
-
-    if nodes_to_ignore is None:
-        nodes_to_ignore = {}
-
-    if nodes_to_test is None:
-        nodes_to_test = dag.nodes
-
-    if threads < 2:
-        metamorphic_relations = [
-            generate_metamorphic_relation(node_pair, dag, nodes_to_ignore)
-            for node_pair in combinations(filter(lambda node: node not in nodes_to_ignore, nodes_to_test), 2)
-        ]
-    else:
-        with Pool(threads) as pool:
-            metamorphic_relations = pool.starmap(
-                generate_metamorphic_relation,
-                map(
-                    lambda node_pair: (node_pair, dag, nodes_to_ignore),
-                    combinations(filter(lambda node: node not in nodes_to_ignore, nodes_to_test), 2),
-                ),
-            )
-
-    return [item for items in metamorphic_relations for item in items]
-
-
-def generate_causal_tests(
-    dag_path: str,
-    output_path: str,
-    ignore_cycles: bool = False,
-    threads: int = 0,
-    test_inputs: bool = False,
-    **json_stub_kargs,
-):
-    """
-    Generate and output causal tests for a given DAG.
-
-    :param dag_path: Path to the DOT file that specifies the causal DAG.
-    :param output_path: Path to save the JSON output.
-    :param ignore_cycles: Whether to bypass the check that the DAG is actually acyclic. If set to true, tests that
-                          include variables that are part of a cycle as either treatment, outcome, or adjustment will
-                          be omitted from the test set.
-    :param threads: The number of threads to use to generate tests in parallel. If unspecified, tests are generated in
-                    serial. This is tylically fine unless the number of tests to be generated is >10000.
-    :param test_inputs: Whether to test independences between inputs (i.e. root nodes in the DAG). Defaults to False
-                        as they will typically be independent by construction.
-    :param json_stub_kargs: Kwargs to pass into `to_json_stub` (see docstring for details.)
-    """
-    causal_dag = CausalDAG(dag_path, ignore_cycles=ignore_cycles)
-
-    dag_nodes_to_test = [
-        node
-        for node in causal_dag.nodes
-        if nx.get_node_attributes(causal_dag, "test", default=True)[node]  # pylint: disable=E1123
-    ]
-
-    if not causal_dag.is_acyclic() and ignore_cycles:
-        logger.warning(
-            "Ignoring cycles by removing causal tests that reference any node within a cycle. "
-            "Your causal test suite WILL NOT BE COMPLETE!"
-        )
-        relations = generate_metamorphic_relations(
-            causal_dag,
-            nodes_to_test=dag_nodes_to_test,
-            nodes_to_ignore=set(causal_dag.cycle_nodes()),
-            threads=threads,
-        )
-    else:
-        relations = generate_metamorphic_relations(causal_dag, nodes_to_test=dag_nodes_to_test, threads=threads)
-
-    tests = [
-        relation.to_json_stub(**json_stub_kargs)
-        for relation in relations
-        if test_inputs or len(list(causal_dag.predecessors(relation.base_test_case.outcome_variable))) > 0
-    ]
-
-    logger.warning(
-        "The skip parameter is hard-coded to False during test generation for better integration with the "
-        "causal testing component (causal-testing test ...)"
-        "Please carefully review the generated tests and decide which to skip."
-    )
-
-    logger.info(f"Generated {len(tests)} tests. Saving to {output_path}.")
-    with open(output_path, "w", encoding="utf-8") as f:
-        json.dump({"tests": tests}, f, indent=2)
diff --git a/docs/source/tutorials/poisson_line_process/poisson_line_process_tutorial.ipynb b/docs/source/tutorials/poisson_line_process/poisson_line_process_tutorial.ipynb
index 4d9b4cc9..6460b21b 100644
--- a/docs/source/tutorials/poisson_line_process/poisson_line_process_tutorial.ipynb
+++ b/docs/source/tutorials/poisson_line_process/poisson_line_process_tutorial.ipynb
@@ -73,7 +73,7 @@
    "id": "15354565-eeb5-4722-bf6b-0b987eabb8c2",
    "metadata": {},
    "source": [
-    "## Step 2: Read in the Data"
+    "## Step 2: Read in the DAG and Data"
    ]
   },
   {
@@ -273,8 +273,10 @@
    ],
    "source": [
     "import pandas as pd\n",
+    "from causal_testing.specification.causal_dag import CausalDAG\n",
     "\n",
     "df = pd.read_csv(data_file, index_col=0)\n",
+    "causal_dag = CausalDAG(dag_file)\n",
     "\n",
     "df"
    ]
@@ -287,63 +289,12 @@
     "In this case, the PLT model has three positive floating-point input parameters: thee width and height of the sampling window, and the intensity of the Poisson process. The model then outputs the total number of lines intersecting the sampling window, and the number of polygons formed by the intersecting lines. Note: in this dataset, the output variables appended by the suffix `_unit` are normalised with respect to their respective areas (`width*height`)."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "b6526dd1-625b-48bb-9782-f80080b1b917",
-   "metadata": {},
-   "source": [
-    "## Step 3: Create a Modelling Scenario"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ac297d2d-5a2f-4c33-bbdc-967d54e24e3f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from causal_testing.specification.variable import Input, Output\n",
-    "from causal_testing.specification.causal_dag import CausalDAG\n",
-    "from causal_testing.specification.scenario import Scenario\n",
-    "\n",
-    "# Define the input variables \n",
-    "\n",
-    "width = Input(\"width\", float) \n",
-    "\n",
-    "height = Input(\"height\", float)\n",
-    "\n",
-    "intensity = Input(\"intensity\", float)\n",
-    "\n",
-    "# Define the output variables \n",
-    "\n",
-    "num_lines_abs = Output(\"num_lines_abs\", float)\n",
-    "\n",
-    "num_lines_unit = Output(\"num_lines_unit\", float)\n",
-    "\n",
-    "num_shapes_abs = Output(\"num_shapes_abs\", float)\n",
-    "\n",
-    "num_shapes_unit = Output(\"num_shapes_unit\", float)\n",
-    "\n",
-    "# Pass these variables into the Scenario class \n",
-    "scenario = Scenario(\n",
-    "    variables={\n",
-    "        width,\n",
-    "        height,\n",
-    "        intensity,\n",
-    "        num_lines_abs,\n",
-    "        num_lines_unit,\n",
-    "        num_shapes_abs,\n",
-    "        num_shapes_unit})\n",
-    "\n",
-    "causal_dag = CausalDAG(dag_file) # Secondly, create the Causal DAG "
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "877d413d-ff96-4481-953f-891c19493531",
    "metadata": {},
    "source": [
-    "## Step 4: Create Causal Test Cases"
+    "## Step 3: Create Causal Test Cases"
    ]
   },
   {
@@ -369,24 +320,6 @@
     "### Metamorphic Relation 1: Doubling the intensity should cause the number of polygons per unit area to increase by a factor of 4"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "9b8491ab-0a90-4061-baee-8e1ecef7371d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from causal_testing.testing.base_test_case import BaseTestCase\n",
-    "from causal_testing.testing.causal_test_case import CausalTestCase\n",
-    "from causal_testing.testing.causal_effect import ExactValue, Positive\n",
-    "from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator\n",
-    "\n",
-    "base_test_case = BaseTestCase(treatment_variable=intensity, outcome_variable=num_shapes_unit) # Create the base test case\n",
-    "\n",
-    "# Perform identification on the DAG using the base test case\n",
-    "adjustment_set = causal_dag.identification(base_test_case) # Note: an empty adjustment set means there are no confounding variables that need to be controlled for"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "e8026067-4df6-43f4-8927-6ac9415b9232",
@@ -397,13 +330,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "fa53a888-68e1-4f6f-babf-16d3a206ea49",
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator\n",
+    "from causal_testing.testing.causal_effect import ExactValue, Positive\n",
+    "from causal_testing.testing.causal_test_case import CausalTestCase\n",
     "\n",
     "control_values, treatment_values = 2 ** np.arange(0, 4), 2 ** np.arange(1, 5) # Initialise the dummy intensity variables\n",
     "\n",
@@ -412,18 +347,19 @@
     "for (control, treatment) in zip(control_values, treatment_values): # Simultaneously loop over control and treatment\n",
     "    \n",
     "    estimator=LinearRegressionEstimator(\n",
-    "                    base_test_case=base_test_case, # Base test case we created above\n",
+    "                    treatment_variable=\"intensity\", # Our treatment variable\n",
+    "                    outcome_variable=\"num_shapes_unit\", # Our outcome variable\n",
     "                    treatment_value=treatment, # Doubled intensity values\n",
     "                    control_value=control, # Baseline intensity values\n",
-    "                    adjustment_set=adjustment_set, # Adjustment set (no confounders in this example)\n",
     "                    formula=\"num_shapes_unit ~ I(intensity ** 2) + intensity - 1\", # Patsy formula describing a linear regression model\n",
-    "                    alpha=0.05) # Significance level\n",
+    "                    alpha=0.05 # Significance level\n",
+    "    )\n",
     "        \n",
     "    causal_test_case = CausalTestCase(\n",
-    "                base_test_case=base_test_case, # Pass in the base test case\n",
     "                expected_causal_effect=ExactValue(4, atol=0.5), # Include a tolerence of 0.5\n",
-    "                estimate_type=\"risk_ratio\", # As described in our paper\n",
-    "                estimator = estimator) # Pass in the estimator we created above\n",
+    "                effect_measure=\"risk_ratio\", # As described in our paper\n",
+    "                estimator = estimator # Pass in the estimator we created above\n",
+    "    )\n",
     "\n",
     "\n",
     "    causal_test_case.execute_test(df) # Execute the tests\n",
@@ -449,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "6bc8be40-bc95-4187-8771-4ce096acc7b5",
    "metadata": {},
    "outputs": [
@@ -526,7 +462,7 @@
        "3      8      16        8         16    3.699311"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -575,7 +511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "67bf5061-720f-4b3a-a371-3ff3092e81e1",
    "metadata": {},
    "outputs": [],
@@ -584,28 +520,24 @@
     "\n",
     "width_results = [] # Empty list for storing test case results \n",
     "\n",
-    "base_test_case = BaseTestCase(treatment_variable=width, outcome_variable=num_shapes_unit) # Create the base test case\n",
-    "\n",
-    "adjustment_set = causal_dag.identification(base_test_case) # Calculate the adjustment set again (if it exists)\n",
-    "\n",
     "for intensity in treatment_values:\n",
     "   \n",
     "  for width_value in control_values:\n",
     "      \n",
     "      estimator = LinearRegressionEstimator(\n",
-    "              base_test_case = base_test_case, # Base test case we created above\n",
+    "              treatment_variable=\"width\", # Our treatment variable\n",
+    "              outcome_variable=\"num_shapes_unit\", # Our outcome variable\n",
     "              treatment_value = width_value + 1.0, # Changing the width\n",
     "              control_value=float(width_value), # Baseline width values\n",
-    "              adjustment_set=adjustment_set, # Use the same adjustment set as list comprehension\n",
-    "              effect_modifiers={\"intensity\": intensity},\n",
+    "              adjustment_config={\"intensity\": intensity},\n",
     "              formula=\"num_shapes_unit ~ width + I(intensity ** 2)+I(width ** -1)+intensity-1\", # Patsy formula describing a linear regression model\n",
     "              alpha=0.05) # Significance level\n",
     "      \n",
     "      causal_test_case = CausalTestCase(\n",
-    "          base_test_case = base_test_case, # Pass in the base test case\n",
     "          expected_causal_effect = Positive(), # We expect a positive increase\n",
-    "          estimate_type = \"ate_calculated\", # Calls the ate_calculated method in the linear regression estimator\n",
-    "          estimator=estimator) # Pass in the estimator we created above\n",
+    "          effect_measure = \"ate_calculated\", # Calls the ate_calculated method in the linear regression estimator\n",
+    "          estimator=estimator # Pass in the estimator we created above\n",
+    "      )\n",
     "      \n",
     "      causal_test_case.execute_test(df) # Execute the tests\n",
     "\n",
@@ -614,7 +546,7 @@
     "           {\n",
     "           \"control\": estimator.control_value,\n",
     "           \"treatment\": estimator.treatment_value,\n",
-    "           \"intensity\": estimator.effect_modifiers[\"intensity\"],\n",
+    "           \"intensity\": estimator.adjustment_config[\"intensity\"],\n",
     "           \"ate\": causal_test_case.result.effect_estimate.value[0],\n",
     "           \"ci_low\": causal_test_case.result.effect_estimate.ci_low[0],\n",
     "           \"ci_high\": causal_test_case.result.effect_estimate.ci_high[0],\n",
@@ -623,7 +555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "6c54392c-4e6b-42b3-b39a-e0d1d0ab25b7",
    "metadata": {},
    "outputs": [
@@ -765,7 +697,7 @@
        "9      1.0        2.0          2 -7.378642 -16.381136   1.623851"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/docs/source/tutorials/vaccinating_elderly/causal_test_results.json b/docs/source/tutorials/vaccinating_elderly/causal_test_results.json
deleted file mode 100644
index 06608b56..00000000
--- a/docs/source/tutorials/vaccinating_elderly/causal_test_results.json
+++ /dev/null
@@ -1,242 +0,0 @@
-[
-  {
-    "name": "max_doses _||_ cum_vaccinations",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "max_doses",
-    "expected_effect": "NoEffect",
-    "alpha": 0.05,
-    "formula": "cum_vaccinations ~ max_doses",
-    "skip": false,
-    "passed": false,
-    "result": {
-      "treatment": "max_doses",
-      "outcome": "cum_vaccinations",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "max_doses": 156420.11333333334
-      },
-      "ci_low": {
-        "max_doses": 131300.57992045846
-      },
-      "ci_high": {
-        "max_doses": 181539.64674620822
-      }
-    }
-  },
-  {
-    "name": "max_doses _||_ cum_vaccinated",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "max_doses",
-    "expected_effect": "NoEffect",
-    "alpha": 0.05,
-    "formula": "cum_vaccinated ~ max_doses",
-    "skip": false,
-    "passed": false,
-    "result": {
-      "treatment": "max_doses",
-      "outcome": "cum_vaccinated",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "max_doses": 116666.9866666667
-      },
-      "ci_low": {
-        "max_doses": 91484.01135222429
-      },
-      "ci_high": {
-        "max_doses": 141849.9619811091
-      }
-    }
-  },
-  {
-    "name": "max_doses _||_ cum_infections",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "max_doses",
-    "expected_effect": "NoEffect",
-    "alpha": 0.05,
-    "formula": "cum_infections ~ max_doses",
-    "skip": false,
-    "passed": false,
-    "result": {
-      "treatment": "max_doses",
-      "outcome": "cum_infections",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "max_doses": 2198.7466666666674
-      },
-      "ci_low": {
-        "max_doses": 2065.1113722575496
-      },
-      "ci_high": {
-        "max_doses": 2332.381961075785
-      }
-    }
-  },
-  {
-    "name": "vaccine --> cum_vaccinations",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "vaccine",
-    "expected_effect": "SomeEffect",
-    "alpha": 0.05,
-    "formula": "cum_vaccinations ~ vaccine",
-    "skip": false,
-    "passed": true,
-    "result": {
-      "treatment": "vaccine",
-      "outcome": "cum_vaccinations",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "vaccine": 482117.166666667
-      },
-      "ci_low": {
-        "vaccine": 481306.02265171934
-      },
-      "ci_high": {
-        "vaccine": 482928.3106816146
-      }
-    }
-  },
-  {
-    "name": "vaccine --> cum_vaccinated",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "vaccine",
-    "expected_effect": "SomeEffect",
-    "alpha": 0.05,
-    "formula": "cum_vaccinated ~ vaccine",
-    "skip": false,
-    "passed": true,
-    "result": {
-      "treatment": "vaccine",
-      "outcome": "cum_vaccinated",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "vaccine": 483334.9333333335
-      },
-      "ci_low": {
-        "vaccine": 482527.15405254293
-      },
-      "ci_high": {
-        "vaccine": 484142.7126141241
-      }
-    }
-  },
-  {
-    "name": "vaccine --> cum_infections",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "vaccine",
-    "expected_effect": "SomeEffect",
-    "alpha": 0.05,
-    "formula": "cum_infections ~ vaccine",
-    "skip": false,
-    "passed": true,
-    "result": {
-      "treatment": "vaccine",
-      "outcome": "cum_infections",
-      "adjustment_set": [],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "vaccine": 2520.466666666669
-      },
-      "ci_low": {
-        "vaccine": 2395.520298064243
-      },
-      "ci_high": {
-        "vaccine": 2645.413035269095
-      }
-    }
-  },
-  {
-    "name": "cum_vaccinations _||_ cum_vaccinated | ['vaccine']",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "cum_vaccinations",
-    "expected_effect": "NoEffect",
-    "alpha": 0.05,
-    "formula": "cum_vaccinated ~ cum_vaccinations+vaccine",
-    "skip": false,
-    "passed": false,
-    "result": {
-      "treatment": "cum_vaccinations",
-      "outcome": "cum_vaccinated",
-      "adjustment_set": [
-        "vaccine"
-      ],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "cum_vaccinations": 0.9955241387494596
-      },
-      "ci_low": {
-        "cum_vaccinations": 0.9887483408376138
-      },
-      "ci_high": {
-        "cum_vaccinations": 1.0022999366613055
-      }
-    }
-  },
-  {
-    "name": "cum_vaccinations _||_ cum_infections | ['vaccine']",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "cum_vaccinations",
-    "expected_effect": "NoEffect",
-    "alpha": 0.05,
-    "formula": "cum_infections ~ cum_vaccinations+vaccine",
-    "skip": false,
-    "passed": true,
-    "result": {
-      "treatment": "cum_vaccinations",
-      "outcome": "cum_infections",
-      "adjustment_set": [
-        "vaccine"
-      ],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "cum_vaccinations": 0.0007674041581016144
-      },
-      "ci_low": {
-        "cum_vaccinations": -0.040087847179842324
-      },
-      "ci_high": {
-        "cum_vaccinations": 0.04162265549604555
-      }
-    }
-  },
-  {
-    "name": "cum_vaccinated _||_ cum_infections | ['vaccine']",
-    "estimate_type": "coefficient",
-    "effect": "direct",
-    "treatment_variable": "cum_vaccinated",
-    "expected_effect": "NoEffect",
-    "alpha": 0.05,
-    "formula": "cum_infections ~ cum_vaccinated+vaccine",
-    "skip": false,
-    "passed": true,
-    "result": {
-      "treatment": "cum_vaccinated",
-      "outcome": "cum_infections",
-      "adjustment_set": [
-        "vaccine"
-      ],
-      "effect_measure": "coefficient",
-      "effect_estimate": {
-        "cum_vaccinated": 0.0017107387725947554
-      },
-      "ci_low": {
-        "cum_vaccinated": -0.03931269141189859
-      },
-      "ci_high": {
-        "cum_vaccinated": 0.0427341689570881
-      }
-    }
-  }
-]
\ No newline at end of file
diff --git a/docs/source/tutorials/vaccinating_elderly/causal_tests.json b/docs/source/tutorials/vaccinating_elderly/causal_tests.json
index ed90732b..3c4656c0 100644
--- a/docs/source/tutorials/vaccinating_elderly/causal_tests.json
+++ b/docs/source/tutorials/vaccinating_elderly/causal_tests.json
@@ -3,114 +3,105 @@
     {
       "name": "max_doses _||_ cum_vaccinations",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "max_doses",
-      "expected_effect": {
-        "cum_vaccinations": "NoEffect"
-      },
-      "formula": "cum_vaccinations ~ max_doses",
+      "outcome_variable": "cum_vaccinations",
+      "expected_effect": {"name": "NoEffect"},
+      "estimator_kwargs": {"formula": "cum_vaccinations ~ max_doses"},
       "alpha": 0.05,
       "skip": false
     },
     {
       "name": "max_doses _||_ cum_vaccinated",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "max_doses",
-      "expected_effect": {
-        "cum_vaccinated": "NoEffect"
-      },
-      "formula": "cum_vaccinated ~ max_doses",
+      "outcome_variable": "cum_vaccinated",
+      "expected_effect": {"name": "NoEffect"},
+      "estimator_kwargs": {"formula": "cum_vaccinated ~ max_doses"},
       "alpha": 0.05,
       "skip": false
     },
     {
       "name": "max_doses _||_ cum_infections",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "max_doses",
-      "expected_effect": {
-        "cum_infections": "NoEffect"
-      },
-      "formula": "cum_infections ~ max_doses",
+      "outcome_variable": "cum_infections",
+      "expected_effect": {"name": "NoEffect"},
+      "estimator_kwargs": {"formula": "cum_infections ~ max_doses"},
       "alpha": 0.05,
       "skip": false
     },
     {
       "name": "vaccine --> cum_vaccinations",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "vaccine",
-      "expected_effect": {
-        "cum_vaccinations": "SomeEffect"
-      },
-      "formula": "cum_vaccinations ~ vaccine",
+      "outcome_variable": "cum_vaccinations",
+      "expected_effect": {"name": "SomeEffect"},
+      "estimator_kwargs": {"formula": "cum_vaccinations ~ vaccine"},
       "skip": false
     },
     {
       "name": "vaccine --> cum_vaccinated",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "vaccine",
-      "expected_effect": {
-        "cum_vaccinated": "SomeEffect"
-      },
-      "formula": "cum_vaccinated ~ vaccine",
+      "outcome_variable": "cum_vaccinated",
+      "expected_effect": {"name": "SomeEffect"},
+      "estimator_kwargs": {"formula": "cum_vaccinated ~ vaccine"},
       "skip": false
     },
     {
       "name": "vaccine --> cum_infections",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "vaccine",
-      "expected_effect": {
-        "cum_infections": "SomeEffect"
-      },
-      "formula": "cum_infections ~ vaccine",
+      "outcome_variable": "cum_infections",
+      "expected_effect": {"name": "SomeEffect"},
+      "estimator_kwargs": {"formula": "cum_infections ~ vaccine"},
       "skip": false
     },
     {
       "name": "cum_vaccinations _||_ cum_vaccinated | ['vaccine']",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "cum_vaccinations",
-      "expected_effect": {
-        "cum_vaccinated": "NoEffect"
-      },
-      "formula": "cum_vaccinated ~ cum_vaccinations + vaccine",
+      "outcome_variable": "cum_vaccinated",
+      "expected_effect": {"name": "NoEffect"},
+      "estimator_kwargs": {"formula": "cum_vaccinated ~ cum_vaccinations + vaccine"},
       "alpha": 0.05,
       "skip": false
     },
     {
       "name": "cum_vaccinations _||_ cum_infections | ['vaccine']",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "cum_vaccinations",
-      "expected_effect": {
-        "cum_infections": "NoEffect"
-      },
-      "formula": "cum_infections ~ cum_vaccinations + vaccine",
+      "outcome_variable": "cum_infections",
+      "expected_effect": {"name": "NoEffect"},
+      "estimator_kwargs": {"formula": "cum_infections ~ cum_vaccinations + vaccine"},
       "alpha": 0.05,
       "skip": false
     },
     {
       "name": "cum_vaccinated _||_ cum_infections | ['vaccine']",
       "estimator": "LinearRegressionEstimator",
-      "estimate_type": "coefficient",
+      "effect_measure": "coefficient",
       "effect": "direct",
       "treatment_variable": "cum_vaccinated",
-      "expected_effect": {
-        "cum_infections": "NoEffect"
-      },
-      "formula": "cum_infections ~ cum_vaccinated + vaccine",
+      "outcome_variable": "cum_infections",
+      "expected_effect": {"name": "NoEffect"},
+      "estimator_kwargs": {"formula": "cum_infections ~ cum_vaccinated + vaccine"},
       "alpha": 0.05,
       "skip": false
     }
diff --git a/docs/source/tutorials/vaccinating_elderly/vaccinating_elderly_tutorial.ipynb b/docs/source/tutorials/vaccinating_elderly/vaccinating_elderly_tutorial.ipynb
index 717d8131..197b1df8 100644
--- a/docs/source/tutorials/vaccinating_elderly/vaccinating_elderly_tutorial.ipynb
+++ b/docs/source/tutorials/vaccinating_elderly/vaccinating_elderly_tutorial.ipynb
@@ -129,19 +129,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2026-04-29 14:55:38 - causal_testing.main - INFO - Setting up Causal Testing Framework...\n",
-      "2026-04-29 14:55:38 - causal_testing.main - INFO - Loading DAG from dag.dot\n",
-      "2026-04-29 14:55:38 - causal_testing.main - INFO - DAG loaded with 5 nodes and 3 edges\n",
-      "2026-04-29 14:55:38 - causal_testing.main - INFO - Loading data from 1 source(s)\n",
-      "2026-04-29 14:55:38 - causal_testing.main - INFO - Initial data shape: (60, 16)\n",
-      "2026-04-29 14:55:38 - causal_testing.main - INFO - Setup completed successfully\n",
-      "2026-04-29 14:55:38 - causal_testing.main - INFO - Loading test configurations from causal_tests.json\n",
-      "2026-04-29 14:55:39 - root - INFO - Running tests in regular mode\n",
-      "2026-04-29 14:55:39 - causal_testing.main - INFO - Running causal tests...\n",
-      "100%|████████████████████████████████████████████| 9/9 [00:00<00:00, 741.98it/s]\n",
-      "2026-04-29 14:55:39 - causal_testing.main - INFO - Saving results to causal_test_results.json\n",
-      "2026-04-29 14:55:39 - causal_testing.main - INFO - Results saved successfully\n",
-      "2026-04-29 14:55:39 - root - INFO - Causal testing completed successfully.\n"
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - Loading DAG from dag.dot\n",
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - DAG loaded with 5 nodes and 3 edges\n",
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - Loading data from 1 source(s)\n",
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - Initial data shape: (60, 16)\n",
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - Loading test configurations from causal_tests.json\n",
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - Running causal tests...\n",
+      "100%|████████████████████████████████████████████| 9/9 [00:00<00:00, 271.76it/s]\n",
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - Saving results to causal_test_results.json\n",
+      "2026-07-24 10:44:22 - causal_testing.causal_testing_framework - INFO - Results saved successfully\n"
      ]
     }
    ],
@@ -165,33 +161,37 @@
    "metadata": {},
    "source": [
     "```json\n",
-    "{\n",
+    "  {\n",
     "    \"name\": \"max_doses _||_ cum_vaccinations\",\n",
-    "    \"estimate_type\": \"coefficient\",\n",
-    "    \"effect\": \"direct\",\n",
-    "    \"treatment_variable\": \"max_doses\",\n",
+    "    \"skip\": false,\n",
+    "    \"effect_measure\": \"coefficient\",\n",
+    "    \"query\": null,\n",
     "    \"expected_effect\": {\n",
-    "      \"cum_vaccinations\": \"NoEffect\"\n",
+    "      \"name\": \"NoEffect\",\n",
+    "      \"effect_type\": \"direct\",\n",
+    "      \"atol\": 0,\n",
+    "      \"ctol\": 0.0\n",
     "    },\n",
-    "    \"estimator_kwargs\": {\n",
-    "      \"formula\": \"cum_vaccinations ~ max_doses\",\n",
+    "    \"estimator\": {\n",
+    "      \"name\": \"LinearRegressionEstimator\",\n",
+    "      \"treatment_variable\": \"max_doses\",\n",
+    "      \"outcome_variable\": \"cum_vaccinations\",\n",
+    "      \"alpha\": 0.05,\n",
+    "      \"adjustment_set\": [],\n",
+    "      \"formula\": \"cum_vaccinations ~ max_doses\"\n",
     "    },\n",
-    "    \"alpha\": 0.05,\n",
-    "    \"skip\": false,\n",
-    "    \"passed\": false,\n",
     "    \"result\": {\n",
-    "      \"treatment\": \"max_doses\",\n",
-    "      \"outcome\": \"cum_vaccinations\",\n",
-    "      \"adjustment_set\": [],\n",
+    "      \"outcome\": \"FAIL\",\n",
+    "      \"passed\": false,\n",
     "      \"effect_measure\": \"coefficient\",\n",
     "      \"effect_estimate\": {\n",
-    "        \"max_doses\": 156420.11333333337\n",
+    "        \"max_doses\": 156420.11333333334\n",
     "      },\n",
     "      \"ci_low\": {\n",
-    "        \"max_doses\": 131300.5799204585\n",
+    "        \"max_doses\": 131300.57992045846\n",
     "      },\n",
     "      \"ci_high\": {\n",
-    "        \"max_doses\": 181539.64674620825\n",
+    "        \"max_doses\": 181539.64674620822\n",
     "      }\n",
     "    }\n",
     "  }\n",
@@ -223,14 +223,6 @@
     "- [Documentation](https://causal-testing-framework.readthedocs.io/en/latest/index.html)\n",
     "- [Paper](https://dl.acm.org/doi/10.1145/3607184)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "80c1a475-cbee-4912-846b-584d252e90b0",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/docs/source/tutorials/visualising_causal_test_results/visualise_causal_test_results.ipynb b/docs/source/tutorials/visualising_causal_test_results/visualise_causal_test_results.ipynb
index 965cbdd2..f44dd7d6 100644
--- a/docs/source/tutorials/visualising_causal_test_results/visualise_causal_test_results.ipynb
+++ b/docs/source/tutorials/visualising_causal_test_results/visualise_causal_test_results.ipynb
@@ -86,7 +86,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x10000 with 9 Axes>"
       ]
@@ -191,19 +191,19 @@
     "\n",
     "dag_copy = dag.copy()\n",
     "\n",
-    "for i, tests in enumerate(causal_tests_results):\n",
-    "    treatment_node  = tests['result']['treatment']\n",
-    "    outcome_node = tests['result']['outcome']\n",
-    "    confounder_node = get_confounder_node(tests['name'])\n",
-    "    title = tests['name']\n",
+    "for i, test in enumerate(causal_tests_results):\n",
+    "    treatment_node  = test['estimator']['treatment_variable']\n",
+    "    outcome_node = test['estimator']['outcome_variable']\n",
+    "    confounder_node = get_confounder_node(test['name'])\n",
+    "    title = test['name']\n",
     "    dag_copy.add_edge(treatment_node, outcome_node)\n",
     "    edge_colours=[]\n",
     "\n",
     "    for edge in dag_copy.edges():\n",
     "        if edge == (treatment_node, outcome_node):\n",
-    "            if not tests['passed']:\n",
+    "            if not test['result']['passed']:\n",
     "                edge_colours.append(\"C3\")\n",
-    "            elif tests['passed']:\n",
+    "            elif test['result']['passed']:\n",
     "                edge_colours.append(\"C2\")\n",
     "            else:\n",
     "                edge_colours.append(\"C9\")\n",
@@ -242,7 +242,7 @@
    "provenance": []
   },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3 (CI)",
    "language": "python",
    "name": "python3"
   },
@@ -256,7 +256,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.15"
   }
  },
  "nbformat": 4,
diff --git a/examples/covasim_/doubling_beta/example_beta.py b/examples/covasim_/doubling_beta/example_beta.py
index 32cb2665..b9b12116 100644
--- a/examples/covasim_/doubling_beta/example_beta.py
+++ b/examples/covasim_/doubling_beta/example_beta.py
@@ -5,11 +5,9 @@
 import matplotlib.pyplot as plt
 import pandas as pd
 import numpy as np
-from causal_testing.specification.variable import Input, Output
 from causal_testing.testing.causal_test_case import CausalTestCase
 from causal_testing.testing.causal_effect import Positive
 from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
 
 
 logger = logging.getLogger(__name__)
@@ -37,19 +35,13 @@ def doubling_beta_CATE_on_csv(
     # Read in the observational data, perform identification
     past_execution_df = pd.read_csv(observational_data_path)
 
-    # 2. Create variables
-    cum_infections = Output("cum_infections", int)
-    beta = Input("beta", float)
-
-    # 5. Create a base test case
-    base_test_case = BaseTestCase(treatment_variable=beta, outcome_variable=cum_infections)
-
     # 6. Create a causal test case
     causal_test_case = CausalTestCase(
-        base_test_case=base_test_case,
-        expected_causal_effect=Positive,
+        expected_causal_effect=Positive(),
+        effect_measure="ate",
         estimator=LinearRegressionEstimator(
-            base_test_case=base_test_case,
+            treatment_variable="beta",
+            outcome_variable="cum_infections",
             treatment_value=0.032,
             control_value=0.016,
             adjustment_set={"avg_age", "contacts"},  # We use custom adjustment set
@@ -62,10 +54,11 @@ def doubling_beta_CATE_on_csv(
 
     # Repeat for association estimate (no adjustment)
     causal_test_case = CausalTestCase(
-        base_test_case=base_test_case,
-        expected_causal_effect=Positive,
+        expected_causal_effect=Positive(),
+        effect_measure="ate",
         estimator=LinearRegressionEstimator(
-            base_test_case=base_test_case,
+            treatment_variable="beta",
+            outcome_variable="cum_infections",
             treatment_value=0.032,
             control_value=0.016,
             adjustment_set=set(),
diff --git a/examples/covasim_/vaccinating_elderly/example_vaccine.py b/examples/covasim_/vaccinating_elderly/example_vaccine.py
index b877b075..eccdd225 100644
--- a/examples/covasim_/vaccinating_elderly/example_vaccine.py
+++ b/examples/covasim_/vaccinating_elderly/example_vaccine.py
@@ -2,11 +2,9 @@
 import logging
 import pandas as pd
 from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.variable import Input, Output
 from causal_testing.testing.causal_test_case import CausalTestCase
 from causal_testing.testing.causal_effect import Positive, Negative, NoEffect
 from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
 
 
 logger = logging.getLogger(__name__)
@@ -22,38 +20,33 @@ def run_test_case(verbose: bool = False):
     :return results_dict: A dictionary containing ATE, 95% CIs, and Test Pass/Fail
     """
 
-    # 1. Read in the Causal DAG
+    # Read in the Causal DAG
     causal_dag = CausalDAG(os.path.join(ROOT, "dag.dot"))
 
-    # 2. Create variables
-    vaccine = Input("vaccine", int)
-    cum_infections = Output("cum_infections", int)
-    cum_vaccinations = Output("cum_vaccinations", int)
-    cum_vaccinated = Output("cum_vaccinated", int)
-    max_doses = Output("max_doses", int)
-
     # 5. Read the previously simulated data
     obs_df = pd.read_csv(os.path.join(ROOT, "simulated_data.csv"))
 
     # 6. Express expected outcomes
     expected_outcome_effects = {
-        cum_infections: Positive(),
-        cum_vaccinations: Negative(),
-        cum_vaccinated: Negative(),
-        max_doses: NoEffect(),
+        "cum_infections": Positive(),
+        "cum_vaccinations": Negative(),
+        "cum_vaccinated": Negative(),
+        "max_doses": NoEffect(),
     }
     results_dict = {"cum_infections": {}, "cum_vaccinations": {}, "cum_vaccinated": {}, "max_doses": {}}
 
     for outcome_variable, expected_effect in expected_outcome_effects.items():
-        base_test_case = BaseTestCase(treatment_variable=vaccine, outcome_variable=outcome_variable)
         causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
             expected_causal_effect=expected_effect,
+            effect_measure="ate",
             estimator=LinearRegressionEstimator(
-                base_test_case=base_test_case,
+                treatment_variable="vaccine",
+                outcome_variable=outcome_variable,
                 treatment_value=1,
                 control_value=0,
-                adjustment_set=causal_dag.identification(base_test_case),
+                adjustment_set=causal_dag.identification(
+                    treatment_variable="vaccine", outcome_variable=outcome_variable
+                ),
             ),
         )
 
@@ -62,15 +55,15 @@ def run_test_case(verbose: bool = False):
         if verbose:
             logging.info("Causation:\n%s", causal_test_case.result)
 
-        results_dict[outcome_variable.name]["ate"] = causal_test_case.result.effect_estimate.value
+        results_dict[outcome_variable]["ate"] = causal_test_case.result.effect_estimate.value
 
-        results_dict[outcome_variable.name]["cis"] = [
+        results_dict[outcome_variable]["cis"] = [
             causal_test_case.result.effect_estimate.ci_low,
             causal_test_case.result.effect_estimate.ci_high,
         ]
 
-        results_dict[outcome_variable.name]["test_passes"] = causal_test_case.expected_causal_effect.apply(
-            causal_test_case.result
+        results_dict[outcome_variable]["test_passes"] = causal_test_case.expected_causal_effect.apply(
+            causal_test_case.result.effect_estimate
         )
 
     return results_dict
diff --git a/examples/lr91/example_max_conductances.py b/examples/lr91/example_max_conductances.py
index ad9d4801..032f87d2 100644
--- a/examples/lr91/example_max_conductances.py
+++ b/examples/lr91/example_max_conductances.py
@@ -2,13 +2,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
 from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.scenario import Scenario
-from causal_testing.specification.variable import Input, Output
 from causal_testing.testing.causal_test_case import CausalTestCase
 from causal_testing.testing.causal_effect import Positive, Negative, NoEffect
 from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
-from matplotlib.pyplot import rcParams
 
 import os
 import logging
@@ -17,6 +13,7 @@
 logging.basicConfig(level=logging.DEBUG, format="%(message)s")
 
 # Uncommenting the code below will make all graphs publication quality but requires a suitable latex installation
+# from matplotlib.pyplot import rcParams
 
 # rc_fonts = {
 #     "font.size": 8,
@@ -38,12 +35,12 @@ def test_sensitivity_analysis():
     # Read in the 200 model runs and define mean value and expected effect
     model_runs = pd.read_csv(f"{ROOT}/data/results.csv")
     conductance_means = {
-        "G_K": (0.5, Positive),
-        "G_b": (0.5, Positive),
-        "G_K1": (0.5, Positive),
-        "G_si": (0.5, Negative),
-        "G_Na": (0.5, NoEffect),
-        "G_Kp": (0.5, NoEffect),
+        "G_K": (0.5, Positive()),
+        "G_b": (0.5, Positive()),
+        "G_K1": (0.5, Positive()),
+        "G_si": (0.5, Negative()),
+        "G_Na": (0.5, NoEffect()),
+        "G_Kp": (0.5, NoEffect()),
     }
 
     # Normalise the inputs as per the original study
@@ -63,8 +60,7 @@ def test_sensitivity_analysis():
         # Perform each causal test for the given input
         for treatment_value in treatment_values:
             mean, oracle = mean_and_oracle
-            conductance_input = Input(conductance_param, float)
-            ate, ci = effects_on_APD90(OBSERVATIONAL_DATA_PATH, conductance_input, 0.5, treatment_value, oracle)
+            ate, ci = effects_on_APD90(OBSERVATIONAL_DATA_PATH, conductance_param, 0.5, treatment_value, oracle)
 
             # Store results
             average_treatment_effects.append(ate)
@@ -83,59 +79,26 @@ def effects_on_APD90(observational_data_path, treatment_var, control_val, treatm
     :param expected_causal_effect: The expected causal effect (Positive, Negative, No Effect).
     :return: ATE for the effect of G_K on APD90
     """
-    # 1. Define Causal DAG
+    # Define Causal DAG
     causal_dag = CausalDAG(f"{ROOT}/dag.dot")
 
-    # 2. Specify all inputs
-    g_na = Input("G_Na", float)
-    g_si = Input("G_si", float)
-    g_k = Input("G_K", float)
-    g_k1 = Input("G_K1", float)
-    g_kp = Input("G_Kp", float)
-    g_b = Input("G_b", float)
-
-    # 3. Specify all outputs
-    max_voltage = Output("max_voltage", float)
-    rest_voltage = Output("rest_voltage", float)
-    max_voltage_gradient = Output("max_voltage_gradient", float)
-    dome_voltage = Output("dome_voltage", float)
-    apd50 = Output("APD50", int)
-    apd90 = Output("APD90", int)
-
-    # 4. Create scenario by applying constraints over a subset of the inputs
-    scenario = Scenario(
-        variables={
-            g_na,
-            g_si,
-            g_k,
-            g_k1,
-            g_kp,
-            g_b,
-            max_voltage,
-            rest_voltage,
-            max_voltage_gradient,
-            dome_voltage,
-            apd50,
-            apd90,
-        },
-        constraints=set(),
-    )
-
-    # 5. Create a causal specification from the scenario and causal DAG
-    base_test_case = BaseTestCase(treatment_var, apd90)
-    # 6. Create a causal test case
+    # Create a causal test case
     causal_test_case = CausalTestCase(
-        base_test_case=base_test_case,
         expected_causal_effect=expected_causal_effect,
+        effect_measure="ate",
         estimator=LinearRegressionEstimator(
-            base_test_case=base_test_case,
+            treatment_variable=treatment_var,
+            outcome_variable="APD90",
             treatment_value=treatment_val,
             control_value=control_val,
-            adjustment_set=causal_dag.identification(base_test_case),
+            adjustment_set=causal_dag.identification(
+                treatment_variable=treatment_var,
+                outcome_variable="APD90",
+            ),
         ),
     )
 
-    # 9. Run the causal test and print results
+    # Run the causal test and print results
     causal_test_case.execute_test(pd.read_csv(observational_data_path))
     logger.info("%s", causal_test_case.result)
     return causal_test_case.result.effect_estimate.value, (
diff --git a/examples/poisson-line-process/example_pure_python.py b/examples/poisson-line-process/example_pure_python.py
index 0db5aadc..b2d3edae 100644
--- a/examples/poisson-line-process/example_pure_python.py
+++ b/examples/poisson-line-process/example_pure_python.py
@@ -5,15 +5,11 @@
 from scipy.stats import bootstrap
 
 from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.scenario import Scenario
-from causal_testing.specification.variable import Input, Output
 from causal_testing.testing.causal_test_case import CausalTestCase
 from causal_testing.testing.causal_effect import ExactValue, Positive
 from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
 from causal_testing.estimation.abstract_estimator import Estimator
 from causal_testing.estimation.effect_estimate import EffectEstimate
-from causal_testing.testing.base_test_case import BaseTestCase
-
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.DEBUG, format="%(message)s")
@@ -36,11 +32,11 @@ def estimate_risk_ratio(self, df: pd.DataFrame) -> EffectEstimate:
         :param df: The data to use.
         :return: The empirical average treatment effect.
         """
-        treatment_variable = self.base_test_case.treatment_variable.name
-        outcome_variable = self.base_test_case.outcome_variable.name
 
-        control_results = df.where(df[treatment_variable] == self.control_value)[outcome_variable].dropna()
-        treatment_results = df.where(df[treatment_variable] == self.treatment_value)[outcome_variable].dropna()
+        control_results = df.where(df[self.treatment_variable] == self.control_value)[self.outcome_variable].dropna()
+        treatment_results = df.where(df[self.treatment_variable] == self.treatment_value)[
+            self.outcome_variable
+        ].dropna()
 
         def risk_ratio(sample1, sample2):
             return sample1.mean() / sample2.mean()
@@ -54,66 +50,38 @@ def risk_ratio(sample1, sample2):
         )
 
 
-# 1. Read in the Causal DAG
+# Read in the Causal DAG
 ROOT = os.path.realpath(os.path.dirname(__file__))
 causal_dag = CausalDAG(f"{ROOT}/dag.dot")
 
-# 2. Create variables
-width = Input("width", float)
-height = Input("height", float)
-intensity = Input("intensity", float)
-
-num_lines_abs = Output("num_lines_abs", float)
-num_lines_unit = Output("num_lines_unit", float)
-num_shapes_abs = Output("num_shapes_abs", float)
-num_shapes_unit = Output("num_shapes_unit", float)
-
-# 3. Create scenario
-scenario = Scenario(
-    variables={
-        width,
-        height,
-        intensity,
-        num_lines_abs,
-        num_lines_unit,
-        num_shapes_abs,
-        num_shapes_unit,
-    }
-)
-
-observational_data_path = f"{ROOT}/data/random/data_random_1000.csv"
+OBSERVATIONAL_DATA_PATH = f"{ROOT}/data/random/data_random_1000.csv"
 
 
 def test_poisson_intensity_num_shapes(save=False):
     intensity_num_shapes_results = []
-    base_test_case = BaseTestCase(treatment_variable=intensity, outcome_variable=num_shapes_unit)
-    observational_df = pd.read_csv(observational_data_path, index_col=0).astype(float)
+    observational_df = pd.read_csv(OBSERVATIONAL_DATA_PATH, index_col=0).astype(float)
     causal_test_cases = [
         (
             CausalTestCase(
-                base_test_case=base_test_case,
                 expected_causal_effect=ExactValue(4, atol=0.5),
-                estimate_type="risk_ratio",
+                effect_measure="risk_ratio",
                 estimator=EmpiricalMeanEstimator(
-                    base_test_case=base_test_case,
+                    treatment_variable="intensity",
+                    outcome_variable="num_shapes_unit",
                     treatment_value=treatment_value,
                     control_value=control_value,
-                    adjustment_set=causal_dag.identification(base_test_case),
-                    effect_modifiers=None,
                     alpha=0.05,
                 ),
             ),
             f"{ROOT}/data/smt_100/data_smt_wh{wh}_100.csv",
             CausalTestCase(
-                base_test_case=base_test_case,
                 expected_causal_effect=ExactValue(4, atol=0.5),
-                estimate_type="risk_ratio",
+                effect_measure="risk_ratio",
                 estimator=LinearRegressionEstimator(
-                    base_test_case=base_test_case,
+                    treatment_variable="intensity",
+                    outcome_variable="num_shapes_unit",
                     treatment_value=treatment_value,
                     control_value=control_value,
-                    adjustment_set=causal_dag.identification(base_test_case),
-                    effect_modifiers=None,
                     formula="num_shapes_unit ~ I(intensity ** 2) + intensity - 1",
                     alpha=0.05,
                 ),
@@ -145,19 +113,17 @@ def test_poisson_intensity_num_shapes(save=False):
 
 
 def test_poisson_width_num_shapes(save=False):
-    base_test_case = BaseTestCase(treatment_variable=width, outcome_variable=num_shapes_unit)
-    df = pd.read_csv(observational_data_path, index_col=0).astype(float)
+    df = pd.read_csv(OBSERVATIONAL_DATA_PATH, index_col=0).astype(float)
     causal_test_cases = [
         CausalTestCase(
-            base_test_case=base_test_case,
             expected_causal_effect=Positive(),
-            estimate_type="ate_calculated",
+            effect_measure="ate_calculated",
             estimator=LinearRegressionEstimator(
-                base_test_case=base_test_case,
+                treatment_variable="width",
+                outcome_variable="num_shapes_unit",
                 treatment_value=w + 1.0,
                 control_value=float(w),
-                adjustment_set=causal_dag.identification(base_test_case),
-                effect_modifiers={"intensity": i},
+                adjustment_config={"intensity": i},
                 formula="num_shapes_unit ~ width + I(intensity ** 2)+I(width ** -1)+intensity-1",
                 alpha=0.05,
             ),
@@ -171,7 +137,7 @@ def test_poisson_width_num_shapes(save=False):
         {
             "control": causal_test.estimator.control_value,
             "treatment": causal_test.estimator.treatment_value,
-            "intensity": causal_test.estimator.effect_modifiers["intensity"],
+            "intensity": causal_test.estimator.adjustment_config["intensity"],
             "ate": causal_test.result.effect_estimate.value[0],
             "ci_low": causal_test.result.effect_estimate.ci_low,
             "ci_high": causal_test.result.effect_estimate.ci_high,
diff --git a/pyproject.toml b/pyproject.toml
index b82173d1..f773ba49 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,7 +23,7 @@ dependencies = [
     "scipy>=1.12.0,<=1.17.1",
     "statsmodels~=0.14",
     "tabulate~=0.9",
-    "pydot~=2.0",
+    "pydot>=2.0",
     "pygad~=3.3",
     "deap~=1.4.1",
     "sympy~=1.14.0",
diff --git a/tests/discovery_tests/test_abstract_discovery.py b/tests/discovery_tests/test_abstract_discovery.py
index 1f9d9e34..049c72fd 100644
--- a/tests/discovery_tests/test_abstract_discovery.py
+++ b/tests/discovery_tests/test_abstract_discovery.py
@@ -2,20 +2,19 @@
 This module tests common causal discovery functionality provided within the abstract_discovery module.
 """
 
+import os
 import unittest
-import pandas as pd
 from tempfile import TemporaryDirectory
-import os
+
+import pandas as pd
 from numpy import nan
 
-from causal_testing.discovery.abstract_discovery import TestResult, Discovery, simple_cycle
-from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.testing.causal_test_result import CausalTestResult
-from causal_testing.testing.causal_test_case import CausalTestCase
+from causal_testing.discovery.abstract_discovery import Discovery, simple_cycle
 from causal_testing.estimation.effect_estimate import EffectEstimate
 from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
-from causal_testing.specification.variable import Input, Output
+from causal_testing.specification.causal_dag import CausalDAG
+from causal_testing.testing.causal_test_case import CausalTestCase
+from causal_testing.testing.causal_test_result import CausalTestResult, TestOutcome
 
 
 class AbstractDiscovery(Discovery):
@@ -32,16 +31,16 @@ def discover(self):
 
 class TestAbstractHillClimber(unittest.TestCase):
     def setUp(self) -> None:
-        self.base_test_case = BaseTestCase(Input("A", float), Output("B", float))
         self.df = pd.DataFrame({"A": [1, 2], "B": [4, 5]})
         self.abstract_discovery = AbstractDiscovery(
             df=self.df,
         )
         self.estimator = LinearRegressionEstimator(
-            base_test_case=self.base_test_case,
+            treatment_variable="A",
+            outcome_variable="B",
             treatment_value=1,
             control_value=0,
-            adjustment_set={},
+            adjustment_set=set(),
         )
 
     def test_simple_cycle(self):
@@ -55,8 +54,13 @@ def test_simple_cycle_no_cycles(self):
         self.assertEqual(simple_cycle(dag), [])
 
     def test_effect_direction_positive(self):
-        causal_test_case = CausalTestCase(base_test_case=self.base_test_case, expected_causal_effect=None)
+        causal_test_case = CausalTestCase(
+            estimator=LinearRegressionEstimator(treatment_variable="A", outcome_variable="B", adjustment_set=set()),
+            effect_measure="ate",
+            expected_causal_effect=None,
+        )
         causal_test_case.result = CausalTestResult(
+            outcome=None,
             effect_estimate=EffectEstimate(
                 type="ate", value=pd.Series(5.05), ci_low=pd.Series(5), ci_high=pd.Series(6)
             ),
@@ -64,8 +68,13 @@ def test_effect_direction_positive(self):
         self.assertEqual(self.abstract_discovery.effect_direction(causal_test_case), "positive")
 
     def test_effect_direction_negative(self):
-        causal_test_case = CausalTestCase(base_test_case=self.base_test_case, expected_causal_effect=None)
+        causal_test_case = CausalTestCase(
+            estimator=LinearRegressionEstimator(treatment_variable="A", outcome_variable="B", adjustment_set=set()),
+            expected_causal_effect=None,
+            effect_measure="ate",
+        )
         causal_test_case.result = CausalTestResult(
+            outcome=None,
             effect_estimate=EffectEstimate(
                 type="ate", value=pd.Series(-5.05), ci_low=pd.Series(-6), ci_high=pd.Series(-5)
             ),
@@ -73,8 +82,13 @@ def test_effect_direction_negative(self):
         self.assertEqual(self.abstract_discovery.effect_direction(causal_test_case), "negative")
 
     def test_effect_direction_none(self):
-        causal_test_case = CausalTestCase(base_test_case=self.base_test_case, expected_causal_effect=None)
+        causal_test_case = CausalTestCase(
+            estimator=LinearRegressionEstimator(treatment_variable="A", outcome_variable="B", adjustment_set=set()),
+            effect_measure="ate",
+            expected_causal_effect=None,
+        )
         causal_test_case.result = CausalTestResult(
+            outcome=None,
             effect_estimate=EffectEstimate(type="ate", value=pd.Series(0), ci_low=pd.Series(-1), ci_high=pd.Series(1)),
         )
         self.assertEqual(self.abstract_discovery.effect_direction(causal_test_case), None)
@@ -146,13 +160,13 @@ def test_write_dot(self):
         dag.add_edges_from([("A", "B"), ("C", "D"), ("E", "F")])
         dag.test_results = pd.DataFrame(
             [  # Edges
-                {"treatment": "A", "outcome": "B", "effect": "positive", "result": TestResult.PASS},
-                {"treatment": "C", "outcome": "D", "effect": "positive", "result": TestResult.FAIL},
-                {"treatment": "E", "outcome": "F", "effect": "None", "result": TestResult.INESTIMABLE},
+                {"treatment": "A", "outcome": "B", "effect": "positive", "result": TestOutcome.PASS},
+                {"treatment": "C", "outcome": "D", "effect": "positive", "result": TestOutcome.FAIL},
+                {"treatment": "E", "outcome": "F", "effect": "None", "result": TestOutcome.INESTIMABLE},
                 # Independences
-                {"treatment": "A", "outcome": "C", "effect": None, "result": TestResult.PASS},
-                {"treatment": "A", "outcome": "D", "effect": "negative", "result": TestResult.FAIL},
-                {"treatment": "A", "outcome": "E", "effect": None, "result": TestResult.INESTIMABLE},
+                {"treatment": "A", "outcome": "C", "effect": None, "result": TestOutcome.PASS},
+                {"treatment": "A", "outcome": "D", "effect": "negative", "result": TestOutcome.FAIL},
+                {"treatment": "A", "outcome": "E", "effect": None, "result": TestOutcome.INESTIMABLE},
             ]
         )
         abstract_discovery = AbstractDiscovery(pd.DataFrame())
@@ -211,61 +225,61 @@ def test_evaluate_tests_inestimable(self):
         expected_results = pd.DataFrame(
             [
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "length_in",
                     "outcome": "large_gauge",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "large_gauge",
                     "outcome": "length_in",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "length_in",
                     "outcome": "color",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "length_in",
                 },
                 {
-                    "result": TestResult.FAIL,
+                    "result": TestOutcome.FAIL,
                     "expected_effect": "SomeEffect",
                     "treatment": "length_in",
                     "outcome": "completed",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "large_gauge",
                     "outcome": "color",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "large_gauge",
                 },
                 {
-                    "result": TestResult.FAIL,
+                    "result": TestOutcome.FAIL,
                     "expected_effect": "SomeEffect",
                     "treatment": "large_gauge",
                     "outcome": "completed",
                 },
                 {
-                    "result": TestResult.INESTIMABLE,
+                    "result": TestOutcome.INESTIMABLE,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "completed",
                 },
                 {
-                    "result": TestResult.INESTIMABLE,
+                    "result": TestOutcome.INESTIMABLE,
                     "expected_effect": "NoEffect",
                     "treatment": "completed",
                     "outcome": "color",
@@ -287,61 +301,61 @@ def test_evaluate_tests(self):
         expected_results = pd.DataFrame(
             [
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "length_in",
                     "outcome": "large_gauge",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "large_gauge",
                     "outcome": "length_in",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "length_in",
                     "outcome": "color",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "length_in",
                 },
                 {
-                    "result": TestResult.FAIL,
+                    "result": TestOutcome.FAIL,
                     "expected_effect": "SomeEffect",
                     "treatment": "length_in",
                     "outcome": "completed",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "large_gauge",
                     "outcome": "color",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "large_gauge",
                 },
                 {
-                    "result": TestResult.FAIL,
+                    "result": TestOutcome.FAIL,
                     "expected_effect": "SomeEffect",
                     "treatment": "large_gauge",
                     "outcome": "completed",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "completed",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "completed",
                     "outcome": "color",
diff --git a/tests/discovery_tests/test_hill_climber_discovery.py b/tests/discovery_tests/test_hill_climber_discovery.py
index 9338e42a..e93600d3 100644
--- a/tests/discovery_tests/test_hill_climber_discovery.py
+++ b/tests/discovery_tests/test_hill_climber_discovery.py
@@ -3,10 +3,13 @@
 """
 
 import unittest
+
 import pandas as pd
+
+from causal_testing.discovery.abstract_discovery import simple_cycle
 from causal_testing.discovery.hill_climber_discovery import HillClimberDiscovery
 from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.discovery.abstract_discovery import TestResult, Discovery, simple_cycle
+from causal_testing.testing.causal_test_result import TestOutcome
 
 
 class TestHillClimber(unittest.TestCase):
@@ -15,70 +18,70 @@ def test_sum_test_outcomes(self):
         test_results = pd.DataFrame(
             [
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "length_in",
                     "outcome": "large_gauge",
                     "effect": "positive",
                 },
                 {
-                    "result": TestResult.INESTIMABLE,
+                    "result": TestOutcome.INESTIMABLE,
                     "expected_effect": "NoEffect",
                     "treatment": "large_gauge",
                     "outcome": "length_in",
                     "effect": None,
                 },
                 {
-                    "result": TestResult.INESTIMABLE,
+                    "result": TestOutcome.INESTIMABLE,
                     "expected_effect": "NoEffect",
                     "treatment": "length_in",
                     "outcome": "color",
                     "effect": None,
                 },
                 {
-                    "result": TestResult.INESTIMABLE,
+                    "result": TestOutcome.INESTIMABLE,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "length_in",
                     "effect": None,
                 },
                 {
-                    "result": TestResult.FAIL,
+                    "result": TestOutcome.FAIL,
                     "expected_effect": "SomeEffect",
                     "treatment": "length_in",
                     "outcome": "completed",
                     "effect": "negative",
                 },
                 {
-                    "result": TestResult.INESTIMABLE,
+                    "result": TestOutcome.INESTIMABLE,
                     "expected_effect": "NoEffect",
                     "treatment": "large_gauge",
                     "outcome": "color",
                     "effect": None,
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "large_gauge",
                     "effect": None,
                 },
                 {
-                    "result": TestResult.FAIL,
+                    "result": TestOutcome.FAIL,
                     "expected_effect": "SomeEffect",
                     "treatment": "large_gauge",
                     "outcome": "completed",
                     "effect": "positive",
                 },
                 {
-                    "result": TestResult.PASS,
+                    "result": TestOutcome.PASS,
                     "expected_effect": "NoEffect",
                     "treatment": "color",
                     "outcome": "completed",
                     "effect": None,
                 },
                 {
-                    "result": TestResult.INESTIMABLE,
+                    "result": TestOutcome.INESTIMABLE,
                     "expected_effect": "NoEffect",
                     "treatment": "completed",
                     "outcome": "color",
@@ -86,13 +89,13 @@ def test_sum_test_outcomes(self):
                 },
             ]
         )
-        expected_results = {TestResult.PASS: 1.5, TestResult.FAIL: 2, TestResult.INESTIMABLE: 2.5}
+        expected_results = {TestOutcome.PASS: 1.5, TestOutcome.FAIL: 2, TestOutcome.INESTIMABLE: 2.5}
         hill_climber = HillClimberDiscovery(pd.DataFrame())
         self.assertEqual(expected_results, hill_climber.sum_test_outcomes(test_results))
 
     def test_sum_test_outcomes_uninitialised(self):
         hill_climber = HillClimberDiscovery(pd.DataFrame())
-        expected_results = {TestResult.PASS: 0, TestResult.FAIL: 0, TestResult.INESTIMABLE: 0}
+        expected_results = {TestOutcome.PASS: 0, TestOutcome.FAIL: 0, TestOutcome.INESTIMABLE: 0}
 
         self.assertEqual(
             expected_results, hill_climber.sum_test_outcomes(pd.DataFrame(columns=["treatment", "outcome", "result"]))
diff --git a/tests/discovery_tests/test_nsga_discovery.py b/tests/discovery_tests/test_nsga_discovery.py
index 1d6d2386..71ee3b00 100644
--- a/tests/discovery_tests/test_nsga_discovery.py
+++ b/tests/discovery_tests/test_nsga_discovery.py
@@ -3,9 +3,11 @@
 """
 
 import unittest
-import numpy as np
+
 import networkx as nx
+import numpy as np
 import pandas as pd
+
 from causal_testing.discovery.nsga_discovery import NSGADiscovery
 from causal_testing.specification.causal_dag import CausalDAG
 
diff --git a/tests/estimation_tests/test_cubic_spline_estimator.py b/tests/estimation_tests/test_cubic_spline_estimator.py
deleted file mode 100644
index 396b190f..00000000
--- a/tests/estimation_tests/test_cubic_spline_estimator.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import unittest
-
-from causal_testing.estimation.cubic_spline_estimator import CubicSplineRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
-from causal_testing.specification.variable import Input, Output
-
-from tests.estimation_tests.test_linear_regression_estimator import load_chapter_11_df
-
-
-class TestCubicSplineRegressionEstimator(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls):
-        super().setUpClass()
-
-    def test_program_11_3_cublic_spline(self):
-        """Test whether the cublic_spline regression implementation produces the same results as program 11.3 (p. 162).
-        https://www.hsph.harvard.edu/miguel-hernan/wp-content/uploads/sites/1268/2023/10/hernanrobins_WhatIf_30sep23.pdf
-        Slightly modified as Hernan et al. use linear regression for this example.
-        """
-
-        df = load_chapter_11_df()
-
-        base_test_case = BaseTestCase(Input("treatments", float), Output("outcomes", float))
-
-        cublic_spline_estimator = CubicSplineRegressionEstimator(
-            base_test_case=base_test_case, treatment_value=1, control_value=0, adjustment_set=set(), basis=3
-        )
-
-        ate_1 = cublic_spline_estimator.estimate_ate_calculated(df).value
-
-        cublic_spline_estimator.treatment_value = 2
-        ate_2 = cublic_spline_estimator.estimate_ate_calculated(df).value
-
-        # Doubling the treatemebnt value should roughly but not exactly double the ATE
-        self.assertNotEqual(ate_1[0] * 2, ate_2[0])
-        self.assertAlmostEqual(ate_1[0] * 2, ate_2[0])
diff --git a/tests/estimation_tests/test_experimental_estimator.py b/tests/estimation_tests/test_experimental_estimator.py
index 9cfa7f86..394afde7 100644
--- a/tests/estimation_tests/test_experimental_estimator.py
+++ b/tests/estimation_tests/test_experimental_estimator.py
@@ -1,7 +1,6 @@
 import unittest
+
 from causal_testing.estimation.experimental_estimator import ExperimentalEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
-from causal_testing.specification.variable import Input, Output
 
 
 class SystemUnderTest:
@@ -35,28 +34,26 @@ class TestExperimentalEstimator(unittest.TestCase):
 
     def test_estimate_ate(self):
         estimator = ConcreteExperimentalEstimator(
-            base_test_case=BaseTestCase(Input("X", float), Output("Y", float)),
+            treatment_variable="X",
+            outcome_variable="Y",
             treatment_value=2,
             control_value=1,
-            adjustment_set={},
+            adjustment_config={},
             alpha=0.05,
             repeats=200,
         )
         effect_estimate = estimator.estimate_ate()
-        print(effect_estimate.value)
-        print(effect_estimate.ci_low)
-        print(effect_estimate.ci_high)
         self.assertEqual(effect_estimate.value["X"], 2)
         self.assertEqual(effect_estimate.ci_low["X"], 2)
         self.assertEqual(effect_estimate.ci_high["X"], 2)
 
     def test_estimate_risk_ratio(self):
         estimator = ConcreteExperimentalEstimator(
-            base_test_case=BaseTestCase(Input("X", float), Output("Y", float)),
+            treatment_variable="X",
+            outcome_variable="Y",
             treatment_value=2,
             control_value=1,
-            adjustment_set={},
-            effect_modifiers={},
+            adjustment_config={},
             alpha=0.05,
             repeats=200,
         )
diff --git a/tests/estimation_tests/test_genetic_programming_regression_fitter.py b/tests/estimation_tests/test_genetic_programming_regression_fitter.py
index b72ddbd3..7d2c9f9b 100644
--- a/tests/estimation_tests/test_genetic_programming_regression_fitter.py
+++ b/tests/estimation_tests/test_genetic_programming_regression_fitter.py
@@ -1,7 +1,9 @@
 import unittest
+
+import deap
 import pandas as pd
 
-from causal_testing.estimation.genetic_programming_regression_fitter import GP
+from causal_testing.estimation.genetic_programming_regression_fitter import GP, mut_insert
 
 
 def root(x):
@@ -10,10 +12,16 @@ def root(x):
 
 class TestGP(unittest.TestCase):
     def test_init_invalid_fun_name(self):
+        """
+        Test that GP raises ValueError if sympy conversions are provided for invalid function names.
+        """
         with self.assertRaises(ValueError):
             GP(df=pd.DataFrame(), features=[], outcome="", max_order=2, sympy_conversions={"power_1": ""})
 
     def test_simplify_string(self):
+        """
+        Test GP simplification
+        """
         gp = GP(
             df=None,
             features=["x1"],
@@ -23,6 +31,9 @@ def test_simplify_string(self):
         self.assertEqual(str(gp.simplify("power_1(x1)")), "x1")
 
     def test_fitness(self):
+        """
+        Test GP fitness function for perfect expression.
+        """
         gp = GP(
             df=pd.DataFrame({"x1": [1, 2, 3], "outcome": [2, 3, 4]}),
             features=["x1"],
@@ -32,6 +43,9 @@ def test_fitness(self):
         self.assertEqual(gp.fitness("add(x1, 1)"), (0,))
 
     def test_fitness_inf(self):
+        """
+        Test that GP returns infinity fitness for incalculable expressions.
+        """
         gp = GP(
             df=pd.DataFrame({"x1": [1, 2, 3], "outcome": [2, 3, 4]}),
             features=["x1"],
@@ -40,3 +54,17 @@ def test_fitness_inf(self):
             extra_operators=[(root, 1)],
         )
         self.assertEqual(gp.fitness("root(-1)"), (float("inf"),))
+
+    def test_mut_insert_no_primitives(self):
+        """Test that mut_insert returns the unmodified expression if there are no
+        primitives of the appropriate type."""
+        pset = deap.gp.PrimitiveSet("MAIN", 1)
+        pset.addPrimitive(lambda x1, x2: x1 + x2, 1, name="add")
+        expression = deap.gp.PrimitiveTree.from_string("add(ARG0, 1)", pset)
+        self.assertEqual(
+            mut_insert(
+                expression,
+                deap.gp.PrimitiveSet("MAIN", 1),
+            ),
+            (expression,),
+        )
diff --git a/tests/estimation_tests/test_instrumental_variable_estimator.py b/tests/estimation_tests/test_instrumental_variable_estimator.py
index 5c86b0ad..a4e1edf3 100644
--- a/tests/estimation_tests/test_instrumental_variable_estimator.py
+++ b/tests/estimation_tests/test_instrumental_variable_estimator.py
@@ -1,10 +1,9 @@
 import unittest
-import pandas as pd
+
 import numpy as np
+import pandas as pd
 
 from causal_testing.estimation.instrumental_variable_estimator import InstrumentalVariableEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
-from causal_testing.specification.variable import Input, Output
 
 
 class TestInstrumentalVariableEstimator(unittest.TestCase):
@@ -24,13 +23,35 @@ def test_estimate_coefficient(self):
         Test we get the correct coefficient.
         """
         iv_estimator = InstrumentalVariableEstimator(
-            base_test_case=BaseTestCase(Input("X", float), Output("Y", float)),
+            treatment_variable="X",
+            outcome_variable="Y",
             treatment_value=None,
             control_value=None,
-            adjustment_set=set(),
             instrument="Z",
         )
         effect_estimate = iv_estimator.estimate_coefficient(self.df)
         self.assertEqual(effect_estimate.value[0], 2)
         self.assertEqual(effect_estimate.ci_low[0], 2)
         self.assertEqual(effect_estimate.ci_high[0], 2)
+
+    def test_to_dict(self):
+        iv_estimator = InstrumentalVariableEstimator(
+            treatment_variable="X",
+            outcome_variable="Y",
+            control_value=0,
+            treatment_value=1,
+            instrument="Z",
+        )
+        self.assertEqual(
+            iv_estimator.to_dict(),
+            {
+                "name": "InstrumentalVariableEstimator",
+                "treatment_variable": "X",
+                "outcome_variable": "Y",
+                "alpha": 0.05,
+                "control_value": 0,
+                "treatment_value": 1,
+                "instrument": "Z",
+                "bootstrap_size": 100,
+            },
+        )
diff --git a/tests/estimation_tests/test_ipcw_estimator.py b/tests/estimation_tests/test_ipcw_estimator.py
index bac1d2c3..0a3711fb 100644
--- a/tests/estimation_tests/test_ipcw_estimator.py
+++ b/tests/estimation_tests/test_ipcw_estimator.py
@@ -1,6 +1,6 @@
 import unittest
+
 import pandas as pd
-from causal_testing.specification.variable import Input, Output
 
 from causal_testing.estimation.ipcw_estimator import IPCWEstimator
 
@@ -11,22 +11,21 @@ class TestIPCWEstimator(unittest.TestCase):
     """
 
     def setUp(self) -> None:
-        self.outcome = Output("outcome", float)
         self.status_column = "ok"
-        self.timesteps_per_intervention = 1
-        self.control_strategy = [[t, "t", 0] for t in range(1, 4, self.timesteps_per_intervention)]
-        self.treatment_strategy = [[t, "t", 1] for t in range(1, 4, self.timesteps_per_intervention)]
+        self.timesteps_per_observation = 1
+        self.control_strategy = [[t, "t", 0] for t in range(1, 4, self.timesteps_per_observation)]
+        self.treatment_strategy = [[t, "t", 1] for t in range(1, 4, self.timesteps_per_observation)]
         self.fit_bl_switch_formula = "xo_t_do ~ time"
         self.df = pd.read_csv("tests/resources/data/temporal_data.csv")
         self.df[self.status_column] = self.df["outcome"] == 1
 
     def test_estimate_hazard_ratio(self):
         estimation_model = IPCWEstimator(
-            self.timesteps_per_intervention,
-            self.control_strategy,
-            self.treatment_strategy,
-            self.outcome,
-            self.status_column,
+            timesteps_per_observation=self.timesteps_per_observation,
+            control_strategy=self.control_strategy,
+            treatment_strategy=self.treatment_strategy,
+            outcome_variable="outcome",
+            status_column=self.status_column,
             fit_bl_switch_formula=self.fit_bl_switch_formula,
             fit_bltd_switch_formula=self.fit_bl_switch_formula,
             eligibility=None,
@@ -36,11 +35,11 @@ def test_estimate_hazard_ratio(self):
 
     def test_invalid_treatment_strategies(self):
         estimation_model = IPCWEstimator(
-            self.timesteps_per_intervention,
-            self.control_strategy,
-            self.treatment_strategy,
-            self.outcome,
-            self.status_column,
+            timesteps_per_observation=self.timesteps_per_observation,
+            control_strategy=self.control_strategy,
+            treatment_strategy=self.treatment_strategy,
+            outcome_variable="outcome",
+            status_column=self.status_column,
             fit_bl_switch_formula=self.fit_bl_switch_formula,
             fit_bltd_switch_formula=self.fit_bl_switch_formula,
             eligibility=None,
@@ -50,11 +49,11 @@ def test_invalid_treatment_strategies(self):
 
     def test_invalid_fault_t_do(self):
         estimation_model = IPCWEstimator(
-            self.timesteps_per_intervention,
-            self.control_strategy,
-            self.treatment_strategy,
-            self.outcome,
-            self.status_column,
+            timesteps_per_observation=self.timesteps_per_observation,
+            control_strategy=self.control_strategy,
+            treatment_strategy=self.treatment_strategy,
+            outcome_variable="outcome",
+            status_column=self.status_column,
             fit_bl_switch_formula=self.fit_bl_switch_formula,
             fit_bltd_switch_formula=self.fit_bl_switch_formula,
             eligibility=None,
@@ -64,11 +63,11 @@ def test_invalid_fault_t_do(self):
 
     def test_no_individual_began_control_strategy(self):
         estimation_model = IPCWEstimator(
-            self.timesteps_per_intervention,
-            self.control_strategy,
-            self.treatment_strategy,
-            self.outcome,
-            self.status_column,
+            timesteps_per_observation=self.timesteps_per_observation,
+            control_strategy=self.control_strategy,
+            treatment_strategy=self.treatment_strategy,
+            outcome_variable="outcome",
+            status_column=self.status_column,
             fit_bl_switch_formula=self.fit_bl_switch_formula,
             fit_bltd_switch_formula=self.fit_bl_switch_formula,
             eligibility=None,
@@ -78,11 +77,11 @@ def test_no_individual_began_control_strategy(self):
 
     def test_no_individual_began_treatment_strategy(self):
         estimation_model = IPCWEstimator(
-            self.timesteps_per_intervention,
-            self.control_strategy,
-            self.treatment_strategy,
-            self.outcome,
-            self.status_column,
+            timesteps_per_observation=self.timesteps_per_observation,
+            control_strategy=self.control_strategy,
+            treatment_strategy=self.treatment_strategy,
+            outcome_variable="outcome",
+            status_column=self.status_column,
             fit_bl_switch_formula=self.fit_bl_switch_formula,
             fit_bltd_switch_formula=self.fit_bl_switch_formula,
             eligibility=None,
@@ -92,11 +91,11 @@ def test_no_individual_began_treatment_strategy(self):
 
     def test_preprocess_data_no_faults(self):
         estimation_model = IPCWEstimator(
-            self.timesteps_per_intervention,
-            self.control_strategy,
-            self.treatment_strategy,
-            self.outcome,
-            self.status_column,
+            timesteps_per_observation=self.timesteps_per_observation,
+            control_strategy=self.control_strategy,
+            treatment_strategy=self.treatment_strategy,
+            outcome_variable="outcome",
+            status_column=self.status_column,
             fit_bl_switch_formula=self.fit_bl_switch_formula,
             fit_bltd_switch_formula=self.fit_bl_switch_formula,
             eligibility=None,
diff --git a/tests/estimation_tests/test_linear_regression_estimator.py b/tests/estimation_tests/test_linear_regression_estimator.py
index 128fcb55..bfc1fca4 100644
--- a/tests/estimation_tests/test_linear_regression_estimator.py
+++ b/tests/estimation_tests/test_linear_regression_estimator.py
@@ -1,12 +1,11 @@
 import unittest
-import pandas as pd
+
 import numpy as np
-from causal_testing.specification.variable import Input, Output
-from causal_testing.utils.validation import CausalValidator
+import pandas as pd
 
-from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
 from causal_testing.estimation.genetic_programming_regression_fitter import reciprocal
-from causal_testing.testing.base_test_case import BaseTestCase
+from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
+from causal_testing.utils.validation import CausalValidator
 
 
 def load_nhefs_df():
@@ -62,13 +61,98 @@ def setUpClass(cls) -> None:
         cls.nhefs_df = load_nhefs_df()
         cls.chapter_11_df = load_chapter_11_df()
         cls.scarf_df = pd.read_csv("tests/resources/data/scarf_data.csv")
-        cls.base_test_case = BaseTestCase(Input("treatments", float), Output("outcomes", float))
-        cls.program_15_base_test_case = BaseTestCase(Input("qsmk", float), Output("wt82_71", float))
+
+    def test_complex_formula_adjustment_set(self):
+        """
+        Test that the adjustment set can be extracted from a complex formula without ignoring variables that share their
+        names with key terms like C (for categoricals) or the treatment variable.
+        """
+        formula = "price ~ depth:color + C(cut, Treatment('Good')) + cr(np.minimum(C, 0.8), df=5, constraints='center')"
+        linear_regression_estimator = LinearRegressionEstimator(
+            treatment_variable="cut", outcome_variable="price", formula=formula
+        )
+        self.assertEqual(linear_regression_estimator.adjustment_set, {"C", "color", "depth"})
+
+    def test_complex_formula_adjustment_set_no_c(self):
+        """
+        Test that key terms are not included in the adjustment set.
+        """
+        formula = "price ~ I(color ** 2) + C(cut, Treatment('Good')) + Q(np.minimum(caret, 0.8))"
+        linear_regression_estimator = LinearRegressionEstimator(
+            treatment_variable="cut", outcome_variable="price", formula=formula
+        )
+        self.assertEqual(linear_regression_estimator.adjustment_set, {"caret", "color"})
+
+    def test_complex_formula_adjustment_set_no_dependent(self):
+        """
+        Test that the outcome variable is prepended to the formula if no dependent variable is specified.
+        """
+        formula = "I(color ** 2) + C(cut, Treatment('Good')) + Q(np.minimum(caret, 0.8))"
+        linear_regression_estimator = LinearRegressionEstimator(
+            treatment_variable="cut", outcome_variable="price", formula=formula
+        )
+        self.assertEqual(linear_regression_estimator.formula, f"price ~ {formula}")
+
+    def test_complex_formula_adjustment_set_wrong_dependent(self):
+        """
+        Test that an error is thrown if there is a mismatch between the outcome variable and the  dependent variable.
+        """
+        formula = "incorrect ~ I(color ** 2) + C(cut, Treatment('Good')) + Q(np.minimum(caret, 0.8))"
+        with self.assertRaises(ValueError) as e:
+            LinearRegressionEstimator(treatment_variable="cut", outcome_variable="price", formula=formula)
+            self.assertEqual(
+                e.exception, "Left hand side of formula incorrect does not match the specified outcome_variable price."
+            )
+
+    def test_formula_from_adjustment_set(self):
+        """
+        Test that the correct formula is built from the adjustment set.
+        """
+        linear_regression_estimator = LinearRegressionEstimator(
+            treatment_variable="cut", outcome_variable="price", adjustment_set={"caret", "color"}
+        )
+        self.assertEqual(linear_regression_estimator.formula, "price ~ cut + caret + color")
+
+    def test_complex_formula_adjustment_set_mismatch(self):
+        """
+        Test that an error is thrown when the adjustment set implied by the formula does not match the one specified.
+        """
+        formula = "price ~ I(color ** 2) + C(cut, Treatment('Good')) + Q(np.minimum(caret, 0.8))"
+        with self.assertRaises(ValueError) as e:
+            LinearRegressionEstimator(
+                treatment_variable="cut", outcome_variable="price", formula=formula, adjustment_set=set()
+            )
+            self.assertEqual(e.exception, f"Specified formula {formula} does not match specified adjustment set set()")
+
+    def test_complex_formula_adjustment_config_mismatch(self):
+        """
+        Test that an error is thrown when the adjustment configuration does not match the adjustment set.
+        """
+        with self.assertRaises(ValueError) as e:
+            LinearRegressionEstimator(
+                treatment_variable="cut",
+                outcome_variable="price",
+                adjustment_set={"caret"},
+                adjustment_config={"color": "green"},
+            )
+            self.assertEqual(
+                e.exception,
+                "Specified configuration for variables [color] which are not in the adjustment set {caret}.",
+            )
+
+    def test_no_formula_of_adjustment_set(self):
+        """
+        Test that an error is thrown when neither the formula nor the adjustment set is specified.
+        """
+        with self.assertRaises(ValueError) as e:
+            LinearRegressionEstimator(treatment_variable="cut", outcome_variable="price")
+            self.assertEqual(e.exception, "Please specify either a formula or an adjustment set.")
 
     def test_linear_regression_categorical_ate(self):
         df = self.scarf_df.copy()
-        base_test_case = BaseTestCase(Input("color", float), Output("completed", float))
-        linear_regression_estimator = LinearRegressionEstimator(base_test_case=base_test_case, adjustment_set=set())
+        linear_regression_estimator = LinearRegressionEstimator(
+            treatment_variable="color", outcome_variable="completed", adjustment_set=set()
+        )
         effect_estimate = linear_regression_estimator.estimate_coefficient(df)
         self.assertTrue(
             all(ci_low < 0 < ci_high for ci_low, ci_high in zip(effect_estimate.ci_low, effect_estimate.ci_high))
@@ -77,7 +161,9 @@ def test_linear_regression_categorical_ate(self):
     def test_program_11_2(self):
         """Test whether our linear regression implementation produces the same results as program 11.2 (p. 141)."""
         df = self.chapter_11_df
-        linear_regression_estimator = LinearRegressionEstimator(self.base_test_case, adjustment_set=set())
+        linear_regression_estimator = LinearRegressionEstimator(
+            treatment_variable="treatments", outcome_variable="outcomes", adjustment_set=set()
+        )
         effect_estimate = linear_regression_estimator.estimate_coefficient(df)
 
         # Increasing treatments from 90 to 100 should be the same as 10 times the unit ATE
@@ -92,8 +178,8 @@ def test_program_11_3(self):
         """Test whether our linear regression implementation produces the same results as program 11.3 (p. 144)."""
         df = self.chapter_11_df.copy()
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=self.base_test_case,
-            adjustment_set=set(),
+            treatment_variable="treatments",
+            outcome_variable="outcomes",
             formula="outcomes ~ treatments + I(treatments ** 2)",
         )
         effect_estimate = linear_regression_estimator.estimate_coefficient(df)
@@ -125,10 +211,10 @@ def test_program_15_1A(self):
             "smokeyrs",
         }
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=self.program_15_base_test_case,
+            treatment_variable="qsmk",
+            outcome_variable="wt82_71",
             treatment_value=1,
             control_value=0,
-            adjustment_set=covariates,
             formula=f"""wt82_71 ~ qsmk +
                              {'+'.join(sorted(list(covariates)))} +
                              I(age ** 2) +
@@ -145,27 +231,11 @@ def test_program_15_no_interaction(self):
         """Test whether our linear regression implementation produces the same results as program 15.1 (p. 163, 184)
         without product parameter."""
         df = self.nhefs_df
-        covariates = {
-            "sex",
-            "race",
-            "age",
-            "edu_2",
-            "edu_3",
-            "edu_4",
-            "edu_5",
-            "exercise_1",
-            "exercise_2",
-            "active_1",
-            "active_2",
-            "wt71",
-            "smokeintensity",
-            "smokeyrs",
-        }
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=self.program_15_base_test_case,
+            treatment_variable="qsmk",
+            outcome_variable="wt82_71",
             treatment_value=1,
             control_value=0,
-            adjustment_set=covariates,
             formula="wt82_71 ~ qsmk + age + I(age ** 2) + wt71 + I(wt71 ** 2) + smokeintensity + I(smokeintensity ** 2) + smokeyrs + I(smokeyrs ** 2)",
         )
         # terms_to_square = ["age", "wt71", "smokeintensity", "smokeyrs"]
@@ -180,27 +250,11 @@ def test_program_15_no_interaction_ate(self):
         """Test whether our linear regression implementation produces the same results as program 15.1 (p. 163, 184)
         without product parameter."""
         df = self.nhefs_df
-        covariates = {
-            "sex",
-            "race",
-            "age",
-            "edu_2",
-            "edu_3",
-            "edu_4",
-            "edu_5",
-            "exercise_1",
-            "exercise_2",
-            "active_1",
-            "active_2",
-            "wt71",
-            "smokeintensity",
-            "smokeyrs",
-        }
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=self.program_15_base_test_case,
+            treatment_variable="qsmk",
+            outcome_variable="wt82_71",
             treatment_value=1,
             control_value=0,
-            adjustment_set=covariates,
             formula="wt82_71 ~ qsmk + age + I(age ** 2) + wt71 + I(wt71 ** 2) + smokeintensity + I(smokeintensity ** 2) + smokeyrs + I(smokeyrs ** 2)",
         )
         # terms_to_square = ["age", "wt71", "smokeintensity", "smokeyrs"]
@@ -212,32 +266,15 @@ def test_program_15_no_interaction_ate(self):
     def test_program_15_no_interaction_ate_calculated(self):
         """Test whether our linear regression implementation produces the same results as program 15.1 (p. 163, 184)
         without product parameter."""
-        covariates = {
-            "sex",
-            "race",
-            "age",
-            "edu_2",
-            "edu_3",
-            "edu_4",
-            "edu_5",
-            "exercise_1",
-            "exercise_2",
-            "active_1",
-            "active_2",
-            "wt71",
-            "smokeintensity",
-            "smokeyrs",
-        }
+        covariates = {"age", "wt71", "smokeintensity", "smokeyrs"}
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=self.program_15_base_test_case,
+            treatment_variable="qsmk",
+            outcome_variable="wt82_71",
             treatment_value=1,
             control_value=0,
-            adjustment_set=covariates,
             formula="wt82_71 ~ qsmk + age + I(age ** 2) + wt71 + I(wt71 ** 2) + smokeintensity + I(smokeintensity ** 2) + smokeyrs + I(smokeyrs ** 2)",
             adjustment_config={k: self.nhefs_df.mean()[k] for k in covariates},
         )
-        # terms_to_square = ["age", "wt71", "smokeintensity", "smokeyrs"]
-        # for term_to_square in terms_to_square:
 
         effect_estimate = linear_regression_estimator.estimate_ate_calculated(df=self.nhefs_df)
         self.assertEqual(round(effect_estimate.value[0], 1), 3.5)
@@ -247,7 +284,8 @@ def test_program_11_2_with_robustness_validation(self):
         """Test whether our linear regression estimator, as used in test_program_11_2 can correctly estimate robustness."""
         df = self.chapter_11_df.copy()
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=self.base_test_case,
+            treatment_variable="treatments",
+            outcome_variable="outcomes",
             treatment_value=100,
             control_value=90,
             adjustment_set=set(),
@@ -258,14 +296,38 @@ def test_program_11_2_with_robustness_validation(self):
             round(cv.estimate_robustness(linear_regression_estimator.fit_model(df))["treatments"], 4), 0.7353
         )
 
+    def test_to_dict(self):
+        linear_regression_estimator = LinearRegressionEstimator(
+            treatment_variable="X",
+            outcome_variable="Y",
+            control_value=0,
+            treatment_value=1,
+            adjustment_set={"Z"},
+            adjustment_config={"Z": 1},
+        )
+        self.assertEqual(
+            linear_regression_estimator.to_dict(),
+            {
+                "name": "LinearRegressionEstimator",
+                "treatment_variable": "X",
+                "outcome_variable": "Y",
+                "alpha": 0.05,
+                "adjustment_set": ["Z"],
+                "formula": "Y ~ X + Z",
+                "adjustment_set": ["Z"],
+                "adjustment_config": {"Z": 1},
+                "control_value": 0,
+                "treatment_value": 1,
+            },
+        )
+
     def test_gp(self):
         df = pd.DataFrame()
         df["X"] = np.arange(10).astype(float)
         df["Y"] = 1 / (df["X"] + 1)
-        print(df)
-        base_test_case = BaseTestCase(Input("X", float), Output("Y", float))
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=base_test_case,
+            treatment_variable="X",
+            outcome_variable="Y",
             treatment_value=0,
             control_value=1,
             adjustment_set=set(),
@@ -281,11 +343,11 @@ def test_gp(self):
 
     def test_gp_power(self):
         df = pd.DataFrame()
-        base_test_case = BaseTestCase(Input("X", float), Output("Y", float))
         df["X"] = np.arange(10)
         df["Y"] = 2 * (df["X"] ** 2)
         linear_regression_estimator = LinearRegressionEstimator(
-            base_test_case=base_test_case,
+            treatment_variable="X",
+            outcome_variable="Y",
             treatment_value=0,
             control_value=1,
             adjustment_set=set(),
@@ -316,25 +378,21 @@ def setUpClass(cls) -> None:
 
     def test_X1_effect(self):
         """When we fix the value of X2 to 0, the effect of X1 on Y should become ~2 (because X2 terms are cancelled)."""
-        base_test_case = BaseTestCase(Input("X1", float), Output("Y", float))
         lr_model = LinearRegressionEstimator(
-            base_test_case=base_test_case,
+            treatment_variable="X1",
+            outcome_variable="Y",
             treatment_value=1,
             control_value=0,
             adjustment_set={"X2"},
-            effect_modifiers={"x2": 0},
+            adjustment_config={"X2": 0},
             formula="Y ~ X1 + X2 + (X1 * X2)",
         )
         effect_estimate = lr_model.estimate_ate(self.df)
         self.assertAlmostEqual(effect_estimate.value[0], 2.0)
 
     def test_categorical_confidence_intervals(self):
-        base_test_case = BaseTestCase(Input("color", float), Output("length_in", float))
         lr_model = LinearRegressionEstimator(
-            base_test_case=base_test_case,
-            control_value=None,
-            treatment_value=None,
-            adjustment_set={},
+            treatment_variable="color", outcome_variable="length_in", adjustment_set=set()
         )
         effect_estimate = lr_model.estimate_coefficient(self.scarf_df)
 
@@ -348,3 +406,28 @@ def test_categorical_confidence_intervals(self):
         self.assertTrue(
             effect_estimate.ci_high.round(2).equals(pd.Series({"color[T.grey]": 23.95, "color[T.orange]": 17.08}))
         )
+
+    def test_program_11_3_linear_regression(self):
+        """Test whether the cublic_spline regression implementation produces the same results as program 11.3 (p. 162).
+        https://www.hsph.harvard.edu/miguel-hernan/wp-content/uploads/sites/1268/2023/10/hernanrobins_WhatIf_30sep23.pdf
+        Slightly modified as Hernan et al. use linear regression for this example.
+        """
+
+        df = load_chapter_11_df()
+
+        cublic_spline_estimator = LinearRegressionEstimator(
+            treatment_variable="treatments",
+            outcome_variable="outcomes",
+            treatment_value=1,
+            control_value=0,
+            formula="outcomes ~ cr(treatments, df=3)",
+        )
+
+        ate_1 = cublic_spline_estimator.estimate_ate_calculated(df).value
+
+        cublic_spline_estimator.treatment_value = 2
+        ate_2 = cublic_spline_estimator.estimate_ate_calculated(df).value
+
+        # Doubling the treatemebnt value should roughly but not exactly double the ATE
+        self.assertNotEqual(ate_1[0] * 2, ate_2[0])
+        self.assertAlmostEqual(ate_1[0] * 2, ate_2[0])
diff --git a/tests/estimation_tests/test_logistic_regression_estimator.py b/tests/estimation_tests/test_logistic_regression_estimator.py
index 1bd77ddf..eed8719f 100644
--- a/tests/estimation_tests/test_logistic_regression_estimator.py
+++ b/tests/estimation_tests/test_logistic_regression_estimator.py
@@ -1,8 +1,8 @@
 import unittest
+
 import pandas as pd
+
 from causal_testing.estimation.logistic_regression_estimator import LogisticRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
-from causal_testing.specification.variable import Input, Output
 
 
 class TestLogisticRegressionEstimator(unittest.TestCase):
@@ -16,7 +16,11 @@ def setUpClass(cls) -> None:
 
     def test_odds_ratio(self):
         logistic_regression_estimator = LogisticRegressionEstimator(
-            BaseTestCase(Input("length_in", float), Output("completed", bool)), 65, 55, set()
+            treatment_variable="length_in",
+            outcome_variable="completed",
+            control_value=65,
+            treatment_value=55,
+            adjustment_set=set(),
         )
         effect_estimate = logistic_regression_estimator.estimate_unit_odds_ratio(self.scarf_df)
         self.assertEqual(round(effect_estimate.value.iloc[0], 4), 0.8948)
diff --git a/tests/estimation_tests/test_multinomial_regression_estimator.py b/tests/estimation_tests/test_multinomial_regression_estimator.py
index fd69ac8a..6b05c63a 100644
--- a/tests/estimation_tests/test_multinomial_regression_estimator.py
+++ b/tests/estimation_tests/test_multinomial_regression_estimator.py
@@ -1,8 +1,8 @@
 import unittest
+
 import pandas as pd
+
 from causal_testing.estimation.multinomial_regression_estimator import MultinomialRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
-from causal_testing.specification.variable import Input, Output
 
 
 class TestMultinomialRegressionEstimator(unittest.TestCase):
@@ -17,21 +17,33 @@ def setUpClass(cls) -> None:
 
     def test_odds_ratio(self):
         multinomial_regression_estimator = MultinomialRegressionEstimator(
-            BaseTestCase(Input("length_in", float), Output("completed", bool)), 65, 55, set()
+            treatment_variable="length_in",
+            outcome_variable="completed",
+            control_value=65,
+            treatment_value=55,
+            adjustment_set=set(),
         )
         effect_estimate = multinomial_regression_estimator.estimate_unit_odds_ratio(self.scarf_df)
         self.assertEqual(round(effect_estimate.value.iloc[0], 4), 0.8948)
 
     def test_odds_ratio_category(self):
         multinomial_regression_estimator = MultinomialRegressionEstimator(
-            BaseTestCase(Input("length_in", float), Output("color", bool)), 65, 55, set()
+            treatment_variable="length_in",
+            outcome_variable="color",
+            control_value=65,
+            treatment_value=55,
+            adjustment_set=set(),
         )
         effect_estimate = multinomial_regression_estimator.estimate_unit_odds_ratio(self.scarf_df)
         self.assertTrue(effect_estimate.value.round(4).equals, pd.Series({"grey": 1.0072, "orange": 0.9668}))
 
     def test_odds_ratio_data(self):
         multinomial_regression_estimator = MultinomialRegressionEstimator(
-            BaseTestCase(Input("length_in", float), Output("completed", bool)), 65, 55, set()
+            treatment_variable="length_in",
+            outcome_variable="completed",
+            control_value=65,
+            treatment_value=55,
+            adjustment_set=set(),
         )
         effect_estimate = multinomial_regression_estimator.estimate_unit_odds_ratio(self.scarf_df)
         self.assertEqual(round(effect_estimate.value.iloc[0], 4), 0.8948)
diff --git a/tests/main_tests/test_ctf.py b/tests/main_tests/test_ctf.py
new file mode 100644
index 00000000..312d3acf
--- /dev/null
+++ b/tests/main_tests/test_ctf.py
@@ -0,0 +1,210 @@
+import json
+import unittest
+from pathlib import Path
+
+import pandas as pd
+
+from causal_testing.causal_testing_framework import CausalTestingFramework
+from causal_testing.specification.causal_dag import CausalDAG
+
+
+class TestCausalTestingFramework(unittest.TestCase):
+    def setUp(self):
+        self.dag_path = "tests/resources/data/dag.dot"
+        self.data_paths = ["tests/resources/data/data.csv"]
+        self.test_cases_path = "tests/resources/data/tests.json"
+        self.output_path = Path("results/results.json")
+        self.include_edges_path = "tests/resources/data/include_edges.dot"
+        self.exclude_edges_path = "tests/resources/data/exclude_edges.dot"
+        self.paths = {
+            "dag_path": self.dag_path,
+            "data_paths": self.data_paths,
+            "test_cases_path": self.test_cases_path,
+        }
+
+    def test_load_data(self):
+        csv_framework = CausalTestingFramework()
+        csv_framework.load_data(self.data_paths)
+
+        pqt_framework = CausalTestingFramework()
+        pqt_framework.load_data([path.replace(".csv", ".pqt") for path in self.data_paths])
+        pd.testing.assert_frame_equal(csv_framework.df, pqt_framework.df)
+
+    def test_load_data_query(self):
+        framework = CausalTestingFramework()
+        framework.load_data(data_paths=self.data_paths)
+        self.assertFalse((framework.df["test_input"] > 4).all())
+
+        framework.load_data(data_paths=self.data_paths, query="test_input > 4")
+        self.assertTrue((framework.df["test_input"] > 4).all())
+
+    def test_load_data_invalid_extension(self):
+        framework = CausalTestingFramework()
+        with self.assertRaises(ValueError):
+            framework.load_data("data.invalid")
+
+    def test_load_tests_before_dag(self):
+        framework = CausalTestingFramework()
+        with self.assertRaises(ValueError):
+            framework.load_test_cases_from_json(self.test_cases_path)
+
+    def test_create_test_case_invalid_estimator(self):
+        framework = CausalTestingFramework()
+        framework.setup(**self.paths)
+        with self.assertRaises(ValueError) as e:
+            framework.create_causal_test(
+                {
+                    "treatment_variable": "test_input",
+                    "outcome_variable": "test_output",
+                    "expected_effect": {"name": "NoEffect"},
+                    "estimator": "InvalidEstimator",
+                }
+            )
+        self.assertEqual(
+            "Unsupported estimator InvalidEstimator. Supported: ['CubicSplineEstimator', 'IPCWEstimator', 'InstrumentalVariableEstimator', 'LinearRegressionEstimator', 'LogisticRegressionEstimator', 'MultinomialRegressionEstimator']. "
+            "If you have implemented a custom estimator, you will need to add this to your entrypoints via your "
+            "pyproject.toml file.",
+            str(e.exception),
+        )
+
+    def test_create_test_case_no_estimator(self):
+        framework = CausalTestingFramework()
+        framework.setup(**self.paths)
+        with self.assertRaises(ValueError) as e:
+            framework.create_causal_test(
+                {
+                    "treatment_variable": "test_input",
+                    "outcome_variable": "test_output",
+                    "expected_effect": {"name": "NoEffect"},
+                }
+            )
+        self.assertEqual(
+            "Test configuration must specify an estimator",
+            str(e.exception),
+        )
+
+    def test_create_test_case_invalid_effect(self):
+        framework = CausalTestingFramework()
+        framework.load_dag(self.dag_path)
+        framework.load_data(self.data_paths)
+        test = {
+            "name": "test1",
+            "treatment_variable": "test_input",
+            "estimator": "LinearRegressionEstimator",
+            "effect_measure": "coefficient",
+            "outcome_variable": "test_output",
+            "expected_effect": {"name": "InvalidEffect"},
+            "estimator_kwargs": {"adjustment_set": []},
+        }
+        with self.assertRaises(ValueError) as e:
+            framework.create_causal_test(test)
+            self.assertEqual(
+                "Unsupported causal effect InvalidEffect. Supported: ['ExactValue', 'Negative', 'NoEffect', 'Positive', 'SomeEffect']. "
+                "If you have implemented a custom causal effect, you will need to add this to your entrypoints via your "
+                "pyproject.toml file.",
+                str(e.exception),
+            )
+
+    def test_create_test_case_effect_kwargs(self):
+        framework = CausalTestingFramework()
+        framework.load_dag(self.dag_path)
+        framework.load_data(self.data_paths)
+        test = {
+            "name": "test1",
+            "treatment_variable": "test_input",
+            "estimator": "LinearRegressionEstimator",
+            "effect_measure": "coefficient",
+            "outcome_variable": "test_output",
+            "expected_effect": {"name": "ExactValue", "value": 4},
+            "estimator_kwargs": {"adjustment_set": []},
+        }
+        test_case = framework.create_causal_test(test)
+        self.assertEqual(test_case.expected_causal_effect.value, 4)
+
+    def test_create_test_case_estimator_kwargs(self):
+        framework = CausalTestingFramework()
+        framework.load_dag(self.dag_path)
+        framework.load_data(self.data_paths)
+        test = {
+            "name": "test1",
+            "treatment_variable": "test_input",
+            "estimator": "InstrumentalVariableEstimator",
+            "effect_measure": "coefficient",
+            "outcome_variable": "test_output",
+            "expected_effect": {"name": "SomeEffect"},
+            "estimator_kwargs": {"instrument": "instrumental_variable"},
+        }
+        test_case = framework.create_causal_test(test)
+        self.assertEqual(test_case.estimator.instrument, "instrumental_variable")
+
+    def test_unloaded_tests(self):
+        framework = CausalTestingFramework()
+        with self.assertRaises(ValueError) as e:
+            framework.run_tests()
+        self.assertEqual("No tests to run.", str(e.exception))
+
+    def test_ctf_exception(self):
+        framework = CausalTestingFramework(self.paths)
+        framework.setup(**self.paths, query="test_input < 0")
+
+        with self.assertRaises(ValueError):
+            framework.run_tests()
+
+    def test_ctf_exception_silent(self):
+        framework = CausalTestingFramework(self.paths)
+        framework.setup(**self.paths, query="test_input < 0")
+        framework.run_tests(silent=True)
+        framework.save_results(self.output_path)
+
+        with open(self.test_cases_path, "r", encoding="utf-8") as f:
+            test_configs = json.load(f)
+
+            non_skipped_configs = [t for t in test_configs["tests"] if not t.get("skip", False)]
+            non_skipped_results = [test.result for test in framework.test_cases if not test.skip]
+
+            self.assertEqual(len(non_skipped_results), len(non_skipped_configs))
+
+            for result in non_skipped_results:
+                self.assertEqual(result.passed, False)
+
+    def test_ctf_evaluate_dag(self):
+        framework = CausalTestingFramework(self.paths)
+        framework.setup(**self.paths)
+        results = framework.evaluate_dag()
+        expected = pd.Series(
+            {
+                "PASS": 1,
+                "FAIL": 0,
+                "INESTIMABLE": 0,
+                "PASS_ci_low": 0,
+                "PASS_ci_high": 1,
+                "FAIL_ci_low": 0,
+                "FAIL_ci_high": 0,
+                "INESTIMABLE_ci_low": 0,
+                "INESTIMABLE_ci_high": 0,
+            }
+        ).sort_index()
+        pd.testing.assert_series_equal(results, expected)
+
+    def test_ctf_evaluate_dag_inestimable(self):
+        framework = CausalTestingFramework()
+        framework.df = pd.read_csv("tests/resources/data/scarf_data.csv", index_col=0).query("length_in > 60")
+        framework.dag = CausalDAG(datatypes=framework.df.dtypes)
+        framework.dag.add_nodes_from(framework.df.columns)
+        framework.test_cases = framework.dag.generate_causal_tests()
+
+        results = framework.evaluate_dag()
+        expected = pd.Series(
+            {
+                "FAIL": 1,
+                "FAIL_ci_high": 2,
+                "FAIL_ci_low": 0,
+                "INESTIMABLE": 1,
+                "INESTIMABLE_ci_high": 1,
+                "INESTIMABLE_ci_low": 0,
+                "PASS": 4,
+                "PASS_ci_high": 4,
+                "PASS_ci_low": 0,
+            }
+        ).sort_index()
+        pd.testing.assert_series_equal(results, expected)
diff --git a/tests/main_tests/test_main.py b/tests/main_tests/test_main.py
index 27b94fb8..eddb4938 100644
--- a/tests/main_tests/test_main.py
+++ b/tests/main_tests/test_main.py
@@ -1,17 +1,16 @@
+import json
+import os
+import shutil
+import tempfile
 import unittest
 from pathlib import Path
-import tempfile
-import os
 from unittest.mock import patch
-import shutil
-import json
-import pandas as pd
 
-from causal_testing.causal_testing_framework import CausalTestingFramework
 from causal_testing.__main__ import main
 
 
-class TestCausalTestingFramework(unittest.TestCase):
+class TestMain(unittest.TestCase):
+
     def setUp(self):
         self.dag_path = "tests/resources/data/dag.dot"
         self.data_paths = ["tests/resources/data/data.csv"]
@@ -19,198 +18,6 @@ def setUp(self):
         self.output_path = Path("results/results.json")
         self.include_edges_path = "tests/resources/data/include_edges.dot"
         self.exclude_edges_path = "tests/resources/data/exclude_edges.dot"
-        self.paths = {
-            "dag_path": self.dag_path,
-            "data_paths": self.data_paths,
-            "test_cases_path": self.test_cases_path,
-        }
-
-    def test_load_data(self):
-        csv_framework = CausalTestingFramework()
-        csv_framework.load_data(self.data_paths)
-
-        pqt_framework = CausalTestingFramework()
-        pqt_framework.load_data([path.replace(".csv", ".pqt") for path in self.data_paths])
-        pd.testing.assert_frame_equal(csv_framework.df, pqt_framework.df)
-
-    def test_load_data_query(self):
-        framework = CausalTestingFramework()
-        framework.load_data(data_paths=self.data_paths)
-        self.assertFalse((framework.df["test_input"] > 4).all())
-
-        framework.load_data(data_paths=self.data_paths, query="test_input > 4")
-        self.assertTrue((framework.df["test_input"] > 4).all())
-
-    def test_load_data_invalid_extension(self):
-        framework = CausalTestingFramework()
-        with self.assertRaises(ValueError):
-            framework.load_data("data.invalid")
-
-    def test_load_dag_missing_node(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        framework.dag.add_node("missing")
-        with self.assertRaises(ValueError):
-            framework.create_variables()
-
-    def test_load_tests_before_dag(self):
-        framework = CausalTestingFramework()
-        with self.assertRaises(ValueError):
-            framework.load_test_cases_from_json(self.test_cases_path)
-
-    def test_create_base_test_case_missing_treatment(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        with self.assertRaises(KeyError) as e:
-            framework.create_base_test(
-                {"treatment_variable": "missing", "expected_effect": {"test_outcome": "NoEffect"}}
-            )
-        self.assertEqual("\"Treatment variable 'missing' not found in inputs or outputs\"", str(e.exception))
-
-    def test_create_base_test_case_missing_estimator(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        with self.assertRaises(ValueError) as e:
-            framework.create_causal_test(
-                {"treatment_variable": "test_input", "expected_effect": {"test_output": "NoEffect"}}
-            )
-        self.assertEqual("Test configuration must specify an estimator", str(e.exception))
-
-    def test_create_test_case_invalid_estimator(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        with self.assertRaises(ValueError) as e:
-            framework.create_causal_test(
-                {
-                    "treatment_variable": "test_input",
-                    "expected_effect": {"test_output": "NoEffect"},
-                    "estimator": "InvalidEstimator",
-                }
-            )
-        self.assertEqual(
-            f"Unsupported estimator InvalidEstimator. Supported: ['CubicSplineEstimator', 'IPCWEstimator', 'InstrumentalVariableEstimator', 'LinearRegressionEstimator', 'LogisticRegressionEstimator', 'MultinomialRegressionEstimator']. "
-            "If you have implemented a custom estimator, you will need to add this to your entrypoints via your "
-            "pyproject.toml file.",
-            str(e.exception),
-        )
-
-    def test_create_test_case_invalid_effect(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        test = {
-            "name": "test1",
-            "treatment_variable": "test_input",
-            "estimator": "LinearRegressionEstimator",
-            "estimate_type": "coefficient",
-            "expected_effect": {"test_output": "InvalidEffect"},
-        }
-        base_test_case = framework.create_base_test(test)
-        with self.assertRaises(ValueError) as e:
-            framework.create_causal_test(test)
-        self.assertEqual(
-            f"Unsupported causal effect InvalidEffect. Supported: ['ExactValue', 'Negative', 'NoEffect', 'Positive', 'SomeEffect']. "
-            "If you have implemented a custom causal effect, you will need to add this to your entrypoints via your "
-            "pyproject.toml file.",
-            str(e.exception),
-        )
-
-    def test_create_test_case_effect_kwargs(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        test = {
-            "name": "test1",
-            "treatment_variable": "test_input",
-            "estimator": "LinearRegressionEstimator",
-            "estimate_type": "coefficient",
-            "expected_effect": {"test_output": "ExactValue"},
-            "effect_kwargs": {"value": 4},
-        }
-        base_test_case = framework.create_base_test(test)
-        test_case = framework.create_causal_test(test)
-        self.assertEqual(test_case.expected_causal_effect.value, 4)
-
-    def test_create_test_case_estimator_kwargs(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        test = {
-            "name": "test1",
-            "treatment_variable": "test_input",
-            "estimator": "InstrumentalVariableEstimator",
-            "estimate_type": "coefficient",
-            "expected_effect": {"test_output": "SomeEffect"},
-            "estimator_kwargs": {"instrument": "instrumental_variable"},
-        }
-        base_test_case = framework.create_base_test(test)
-        test_case = framework.create_causal_test(test)
-        self.assertEqual(test_case.estimator.instrument, "instrumental_variable")
-
-    def test_create_base_test_case_missing_outcome(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        with self.assertRaises(KeyError) as e:
-            framework.create_base_test({"treatment_variable": "test_input", "expected_effect": {"missing": "NoEffect"}})
-        self.assertEqual("\"Outcome variable 'missing' not found in inputs or outputs\"", str(e.exception))
-
-    def test_unloaded_tests(self):
-        framework = CausalTestingFramework()
-        with self.assertRaises(ValueError) as e:
-            framework.run_tests()
-        self.assertEqual("No tests to run.", str(e.exception))
-
-    def test_ctf(self):
-        framework = CausalTestingFramework()
-        framework.setup(**self.paths)
-        framework.run_tests()
-        json_results = framework.save_results(self.output_path)
-
-        with open(self.test_cases_path, "r", encoding="utf-8") as f:
-            test_configs = json.load(f)
-
-        self.assertEqual(len(json_results), len(test_configs["tests"]))
-
-        result_index = 0
-        for i, test_config in enumerate(test_configs["tests"]):
-            result = json_results[i]
-
-            if test_config.get("skip", False):
-                self.assertEqual(result["skip"], True)
-                self.assertEqual(result["passed"], None)
-                self.assertEqual(result["result"]["status"], "skipped")
-            else:
-                test_case = framework.test_cases[result_index]
-                result_index += 1
-
-                test_passed = (
-                    test_case.expected_causal_effect.apply(test_case.result)
-                    if test_case.result.effect_estimate is not None
-                    else False
-                )
-                self.assertEqual(result["passed"], test_passed)
-
-    def test_ctf_exception(self):
-        framework = CausalTestingFramework(self.paths)
-        framework.setup(**self.paths, query="test_input < 0")
-
-        with self.assertRaises(ValueError):
-            framework.run_tests()
-
-    def test_ctf_exception_silent(self):
-        framework = CausalTestingFramework(self.paths)
-        framework.setup(**self.paths, query="test_input < 0")
-
-        framework.run_tests(silent=True)
-        json_results = framework.save_results(self.output_path)
-
-        with open(self.test_cases_path, "r", encoding="utf-8") as f:
-            test_configs = json.load(f)
-
-            non_skipped_configs = [t for t in test_configs["tests"] if not t.get("skip", False)]
-            non_skipped_results = [r for r in json_results if not r.get("skip", False)]
-
-            self.assertEqual(len(non_skipped_results), len(non_skipped_configs))
-
-            for result in non_skipped_results:
-                self.assertEqual(result["passed"], False)
 
     def test_parse_args(self):
         with patch(
@@ -245,11 +52,11 @@ def test_parse_args_adequacy(self):
                 str(self.test_cases_path),
                 "--output",
                 str(self.output_path.parent / "main.json"),
-                "-a",
+                "-A",
             ],
         ):
             main()
-            with open(self.output_path.parent / "main.json") as f:
+            with open(self.output_path.parent / "main.json", encoding="utf-8") as f:
                 log = json.load(f)
             executed_tests = [test for test in log if not test.get("skip", False)]
             assert all(test["result"].get("bootstrap_size", 100) == 100 for test in executed_tests)
@@ -273,7 +80,7 @@ def test_parse_args_bootstrap_size(self):
             ],
         ):
             main()
-            with open(self.output_path.parent / "main.json") as f:
+            with open(self.output_path.parent / "main.json", encoding="utf-8") as f:
                 log = json.load(f)
             executed_tests = [test for test in log if not test.get("skip", False)]
             assert all(test["result"].get("bootstrap_size", 50) == 50 for test in executed_tests)
@@ -292,13 +99,13 @@ def test_parse_args_bootstrap_size_explicit_adequacy(self):
                 str(self.test_cases_path),
                 "--output",
                 str(self.output_path.parent / "main.json"),
-                "-a",
+                "-A",
                 "-b",
                 "50",
             ],
         ):
             main()
-            with open(self.output_path.parent / "main.json") as f:
+            with open(self.output_path.parent / "main.json", encoding="utf-8") as f:
                 log = json.load(f)
             executed_tests = [test for test in log if not test.get("skip", False)]
             assert all(test["result"].get("bootstrap_size", 50) == 50 for test in executed_tests)
@@ -312,6 +119,8 @@ def test_parse_args_generation(self):
                     "generate",
                     "--dag-path",
                     str(self.dag_path),
+                    "--data-paths",
+                    str(self.data_paths[0]),
                     "--output",
                     os.path.join(tmp, "tests.json"),
                 ],
@@ -319,29 +128,34 @@ def test_parse_args_generation(self):
                 main()
                 self.assertTrue(os.path.exists(os.path.join(tmp, "tests.json")))
 
-    def test_parse_args_generation_non_default(self):
+    def test_parse_args_discover(self):
         with tempfile.TemporaryDirectory() as tmp:
             with patch(
                 "sys.argv",
                 [
                     "causal_testing",
-                    "generate",
-                    "--dag-path",
-                    str(self.dag_path),
+                    "discover",
+                    "--technique",
+                    "HillClimberDiscovery",
+                    "--data-paths",
+                    str(self.data_paths[0]),
                     "--output",
-                    os.path.join(tmp, "tests_non_default.json"),
-                    "--estimator",
-                    "LogisticRegressionEstimator",
-                    "--estimate-type",
-                    "unit_odds_ratio",
-                    "--effect-type",
-                    "total",
+                    os.path.join(tmp, "discovered_dag.dot"),
+                    "--include-edges",
+                    self.include_edges_path,
+                    "--exclude-edges",
+                    self.exclude_edges_path,
+                    "--technique-kwargs",
+                    "max_iterations 10",
                 ],
             ):
-                main()
-                self.assertTrue(os.path.exists(os.path.join(tmp, "tests_non_default.json")))
+                with self.assertRaises(ValueError) as e:
+                    main()
+                    self.assertEqual(
+                        e.message, "Malformed argument max_iterations. Should be specified as `arg_name=arg_value`"
+                    )
 
-    def test_parse_args_discover(self):
+    def test_parse_args_discover_malformed_argument(self):
         with tempfile.TemporaryDirectory() as tmp:
             with patch(
                 "sys.argv",
@@ -352,7 +166,6 @@ def test_parse_args_discover(self):
                     "HillClimberDiscovery",
                     "--data-paths",
                     str(self.data_paths[0]),
-                    str(self.data_paths[0]),
                     "--output",
                     os.path.join(tmp, "discovered_dag.dot"),
                     "--include-edges",
@@ -369,6 +182,95 @@ def test_parse_args_discover(self):
                 main()
                 self.assertTrue(os.path.exists(os.path.join(tmp, "discovered_dag.dot")))
 
+    def test_parse_args_discover_invalid_technique(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            with patch(
+                "sys.argv",
+                [
+                    "causal_testing",
+                    "discover",
+                    "--technique",
+                    "Invalid",
+                    "--data-paths",
+                    str(self.data_paths[0]),
+                    "--output",
+                    os.path.join(tmp, "discovered_dag.dot"),
+                    "--include-edges",
+                    self.include_edges_path,
+                    "--exclude-edges",
+                    self.exclude_edges_path,
+                    "--technique-kwargs",
+                    "max_iterations=10",
+                    "--variables",
+                    "test_input",
+                    "test_output",
+                ],
+            ):
+                with self.assertRaises(ValueError) as e:
+                    main()
+                    self.assertTrue(e.message.startswith("Unsupported technique Invalid."))
+
+    def test_parse_args_discover_drop_unnamed(self):
+        """
+        Test that the user is warned of the dropping of unnamed columns.
+        """
+        with tempfile.TemporaryDirectory() as tmp:
+            with patch(
+                "sys.argv",
+                [
+                    "causal_testing",
+                    "discover",
+                    "--technique",
+                    "HillClimberDiscovery",
+                    "--data-paths",
+                    str(self.data_paths[0]),
+                    "--output",
+                    os.path.join(tmp, "discovered_dag.dot"),
+                    "--technique-kwargs",
+                    "max_iterations=10",
+                ],
+            ):
+                with self.assertWarnsRegex(UserWarning, r"Dropping unnamed columns: \['Unnamed: 0'\]"):
+                    main()
+
+    def test_parse_args_evaluation_create_tests(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            with patch(
+                "sys.argv",
+                [
+                    "causal_testing",
+                    "evaluate",
+                    "--dag-path",
+                    str(self.dag_path),
+                    "--data-paths",
+                    str(self.data_paths[0]),
+                    "--output",
+                    os.path.join(tmp, "results.csv"),
+                ],
+            ):
+                main()
+                self.assertTrue(os.path.exists(os.path.join(tmp, "results.csv")))
+
+    def test_parse_args_evaluation_read_tests(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            with patch(
+                "sys.argv",
+                [
+                    "causal_testing",
+                    "evaluate",
+                    "--dag-path",
+                    str(self.dag_path),
+                    "--data-paths",
+                    str(self.data_paths[0]),
+                    "--test-config",
+                    str(self.test_cases_path),
+                    "--output",
+                    os.path.join(tmp, "results.csv"),
+                ],
+            ):
+                main()
+                self.assertTrue(os.path.exists(os.path.join(tmp, "results.csv")))
+
     def tearDown(self):
         if self.output_path.parent.exists():
             shutil.rmtree(self.output_path.parent)
diff --git a/tests/resources/data/data.csv b/tests/resources/data/data.csv
index ec2d6002..45a8f45b 100644
--- a/tests/resources/data/data.csv
+++ b/tests/resources/data/data.csv
@@ -1,6 +1,6 @@
-test_input,test_input_no_dist,test_output,B,C
-1.0,1.1,2.2,0,0
-2.0,1.1,2.8,0,0
-3.0,1.0,1.0,0,0
-4.0,1.2,6.0,0,0
-5.0,0.9,2.5,0,0
+,test_input,test_input_no_dist,test_output,B,C
+0,1.0,1.1,2.2,0,0
+1,2.0,1.1,2.8,0,0
+2,3.0,1.0,1.0,0,0
+3,4.0,1.2,6.0,0,0
+4,5.0,0.9,2.5,0,0
diff --git a/tests/resources/data/data.pqt b/tests/resources/data/data.pqt
index 526a04e6..ac247d07 100644
Binary files a/tests/resources/data/data.pqt and b/tests/resources/data/data.pqt differ
diff --git a/tests/resources/data/data_with_meta.csv b/tests/resources/data/data_with_meta.csv
deleted file mode 100644
index 23ed882e..00000000
--- a/tests/resources/data/data_with_meta.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-index,test_input,test_input_no_dist,test_output,test_meta
-0,1.0,1.0,2.0,3
diff --git a/tests/resources/data/tests.json b/tests/resources/data/tests.json
index 54500c43..9ee0171f 100644
--- a/tests/resources/data/tests.json
+++ b/tests/resources/data/tests.json
@@ -3,24 +3,21 @@
     "name": "test1",
     "treatment_variable": "test_input",
     "estimator": "LinearRegressionEstimator",
-    "estimate_type": "coefficient",
-    "expected_effect": {"test_output": "NoEffect"},
+    "effect_measure": "coefficient",
+    "outcome_variable": "test_output", "expected_effect": {"name": "NoEffect"},
     "skip": false,
     "query": "test_input > 0",
-    "estimator_kwargs": {
-      "effect_modifiers": []
-    }
+    "estimator_kwargs": {"adjustment_set": []}
+
   },
   {
     "name": "test2",
     "treatment_variable": "test_input",
     "estimator": "LinearRegressionEstimator",
-    "estimate_type": "coefficient",
-    "expected_effect": {"test_output": "NoEffect"},
+    "effect_measure": "coefficient",
+    "outcome_variable": "test_output", "expected_effect": {"name": "NoEffect"},
     "skip": true,
     "query": "test_input <= 5",
-    "estimator_kwargs": {
-      "effect_modifiers": []
-    }
+    "estimator_kwargs": {"adjustment_set": []}
   }]
 }
diff --git a/tests/specification_tests/test_causal_dag.py b/tests/specification_tests/test_causal_dag.py
index f73ab4d3..4c219415 100644
--- a/tests/specification_tests/test_causal_dag.py
+++ b/tests/specification_tests/test_causal_dag.py
@@ -1,12 +1,11 @@
-import unittest
 import os
 import shutil
 import tempfile
+import unittest
+
 import networkx as nx
+
 from causal_testing.specification.causal_dag import CausalDAG, close_separator, list_all_min_sep
-from causal_testing.specification.scenario import Scenario
-from causal_testing.specification.variable import Input, Output
-from causal_testing.testing.base_test_case import BaseTestCase
 
 
 class TestCausalDAGIssue90(unittest.TestCase):
@@ -27,6 +26,11 @@ def test_graphml(self):
         self.assertEqual(dot_dag.nodes, xml_dag.nodes)
         self.assertEqual(dot_dag.edges, xml_dag.edges)
 
+    def test_invalid_file_extension(self):
+        with self.assertRaises(ValueError) as e:
+            CausalDAG("test.csv")
+            self.assertEqual(e.exception, "Unsupported file extension test.csv. We only support .dot and .xml files.")
+
     def test_enumerate_minimal_adjustment_sets(self):
         """Test whether enumerate_minimal_adjustment_sets lists all possible minimum sized adjustment sets."""
         causal_dag = CausalDAG(self.dag_dot_path)
@@ -135,8 +139,7 @@ def test_invalid_causal_dag(self):
 
     def test_ignore_cycles(self):
         dag = CausalDAG(self.dag_dot_path, ignore_cycles=True)
-        base_test_case = BaseTestCase(Output("B", float), Output("C", float))
-        self.assertEqual(dag.identification(base_test_case), {"A"})
+        self.assertEqual(dag.identification(treatment_variable="B", outcome_variable="C"), {"A"})
 
     def tearDown(self) -> None:
         shutil.rmtree(self.temp_dir_path)
@@ -288,6 +291,21 @@ def test_enumerate_minimal_adjustment_sets(self):
         adjustment_sets = causal_dag.enumerate_minimal_adjustment_sets(xs, ys)
         self.assertEqual([{"Z"}], list(adjustment_sets))
 
+    def test_identification_total_effect(self):
+        """Test whether identification works for total effect."""
+        causal_dag = CausalDAG()
+        causal_dag.add_edges_from([("X", "M"), ("M", "Y")])
+
+        self.assertEqual(
+            set(), causal_dag.identification(treatment_variable="X", outcome_variable="Y", effect_type="total")
+        )
+
+    def test_identification_invalid_effect(self):
+        causal_dag = CausalDAG()
+        with self.assertRaises(ValueError) as e:
+            causal_dag.identification(treatment_variable="X", outcome_variable="Y", effect_type="invalid")
+            self.assertEqual(e.exception, f"Causal effect should be 'total' or 'direct', not 'invalid'.")
+
     def test_enumerate_minimal_adjustment_sets_multiple(self):
         """Test whether enumerate_minimal_adjustment_sets lists all minimum adjustment sets if multiple are possible."""
         causal_dag = CausalDAG()
@@ -400,36 +418,48 @@ def test_list_all_min_sep(self):
         min_separators = set(frozenset(min_separator) for min_separator in min_separators)
         self.assertEqual({frozenset({2, 3}), frozenset({3, 4}), frozenset({4, 5})}, min_separators)
 
+    def test_close_separator_exception(self):
+        g = nx.Graph()
+        g.add_edges_from([("X", "Y")])
+
+        with self.assertRaises(ValueError) as e:
+            close_separator(
+                graph=g,
+                treatment_node="X",
+                outcome_node="X",
+                treatment_node_set={"Y"},
+            )
+            self.assertEqual(e.exception, "No X-Y separator in the graph.")
+
 
 class TestHiddenVariableDAG(unittest.TestCase):
     """
     Test the CausalDAG identification for the exclusion of hidden variables.
     """
 
-    def setUp(self) -> None:
-        self.temp_dir_path = tempfile.mkdtemp()
-        self.dag_dot_path = os.path.join(self.temp_dir_path, "dag.dot")
-        dag_dot = """digraph DAG { rankdir=LR; Z -> X; X -> M; M -> Y; Z -> M; }"""
-        with open(self.dag_dot_path, "w") as f:
-            f.write(dag_dot)
-
-    def test_hidden_varaible_adjustment_sets(self):
-        """Test whether identification produces different adjustment sets depending on if a variable is hidden."""
-        causal_dag = CausalDAG(self.dag_dot_path)
-        z = Input("Z", int)
-        x = Input("X", int)
-        m = Input("M", int)
+    def test_impossible_identification(self):
+        """Test whether identification produces different adjustment sets if nodes_to_ignore is set."""
+        causal_dag = CausalDAG()
+        causal_dag.add_edges_from([("X", "M"), ("M", "Y"), ("X", "Y")])
 
-        scenario = Scenario(variables={z, x, m})
-        adjustment_sets = causal_dag.identification(BaseTestCase(x, m), scenario.hidden_variables())
+        self.assertEqual(causal_dag.identification(treatment_variable="X", outcome_variable="Y"), {"M"})
 
-        z.hidden = True
-        adjustment_sets_with_hidden = causal_dag.identification(BaseTestCase(x, m), scenario.hidden_variables())
+        with self.assertRaises(ValueError) as e:
+            causal_dag.identification(treatment_variable="X", outcome_variable="Y", nodes_to_ignore=["M"])
+            self.assertEqual(
+                e.exception,
+                "Could not find a suitable adjustment set for the direct effect of X on Y while avoiding nodes in set {M}.",
+            )
 
-        self.assertNotEqual(adjustment_sets, adjustment_sets_with_hidden)
+    def test_adjustment_set_nodes_to_ignore(self):
+        """Test whether identification produces different adjustment sets if nodes_to_ignore is set."""
+        causal_dag = CausalDAG()
+        causal_dag.add_edges_from([("L", "V"), ("V", "X"), ("X", "Y"), ("L", "C"), ("C", "Y")])
 
-    def tearDown(self) -> None:
-        shutil.rmtree(self.temp_dir_path)
+        self.assertEqual(causal_dag.identification(treatment_variable="X", outcome_variable="Y"), {"C"})
+        self.assertEqual(
+            causal_dag.identification(treatment_variable="X", outcome_variable="Y", nodes_to_ignore={"C"}), {"L"}
+        )
 
 
 def time_it(label, func, *args, **kwargs):
diff --git a/tests/specification_tests/test_generate_causal_tests.py b/tests/specification_tests/test_generate_causal_tests.py
new file mode 100644
index 00000000..4f453c16
--- /dev/null
+++ b/tests/specification_tests/test_generate_causal_tests.py
@@ -0,0 +1,167 @@
+import os
+import shutil
+import tempfile
+import unittest
+
+from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
+from causal_testing.specification.causal_dag import CausalDAG
+from causal_testing.testing.causal_effect import NoEffect, SomeEffect
+from causal_testing.testing.causal_test_case import CausalTestCase
+
+
+def sort_test_dict(test: dict):
+    return test["name"]
+
+
+class TestGenerateCausalTestCases(unittest.TestCase):
+    def setUp(self) -> None:
+        self.temp_dir_path = tempfile.mkdtemp()
+        self.dag_dot_path = os.path.join(self.temp_dir_path, "dag.dot")
+        dag_dot = """digraph DAG { rankdir=LR; X1 -> Z; Z -> M; M -> Y; X2 -> Z; X3 -> M;}"""
+        with open(self.dag_dot_path, "w") as f:
+            f.write(dag_dot)
+        self.dcg_dot_path = os.path.join(self.temp_dir_path, "dcg.dot")
+        dcg_dot = """digraph dct { a -> b -> c -> d; d -> c; }"""
+        with open(self.dcg_dot_path, "w") as f:
+            f.write(dcg_dot)
+
+        self.default_control_input_config = {"X1": 1, "X2": 2, "X3": 3}
+        self.default_treatment_input_config = {"X1": 2, "X2": 3, "X3": 3}
+
+    def tearDown(self) -> None:
+        shutil.rmtree(self.temp_dir_path)
+
+    def test_all_metamorphic_relations_implied_by_dag(self):
+        dag = CausalDAG(self.dag_dot_path, datatypes={v: float for v in {"X1", "X2", "X3", "Y", "Z", "M"}})
+        dag.add_edge("Z", "Y")  # Add a direct path from Z to Y so M becomes a mediator
+
+        expected_tests = []
+        for treatment, outcome in dag.edges:
+            expected_tests.append(
+                CausalTestCase(
+                    expected_causal_effect=SomeEffect(),
+                    effect_measure="coefficient",
+                    estimator=LinearRegressionEstimator(
+                        treatment_variable=treatment,
+                        outcome_variable=outcome,
+                        adjustment_set=dag.identification(treatment_variable=treatment, outcome_variable=outcome),
+                    ),
+                    name=f"{treatment} -> {outcome}",
+                    skip=False,
+                )
+            )
+        for treatment, outcome in [
+            ("X1", "M"),
+            ("X1", "Y"),
+            ("X1", "X2"),
+            ("X2", "X1"),
+            ("X1", "X3"),
+            ("X3", "X1"),
+            ("Z", "X3"),
+            ("X3", "Z"),
+            ("X2", "M"),
+            ("X2", "Y"),
+            ("X3", "Y"),
+            ("X2", "X3"),
+            ("X3", "X2"),
+        ]:
+            expected_tests.append(
+                CausalTestCase(
+                    expected_causal_effect=NoEffect(),
+                    effect_measure="coefficient",
+                    estimator=LinearRegressionEstimator(
+                        treatment_variable=treatment,
+                        outcome_variable=outcome,
+                        adjustment_set=dag.identification(treatment_variable=treatment, outcome_variable=outcome),
+                    ),
+                    name=f"{treatment} _||_ {outcome}",
+                    skip=False,
+                )
+            )
+
+        self.assertEqual(
+            sorted(map(lambda t: t.to_dict(), expected_tests), key=sort_test_dict),
+            sorted(map(lambda t: t.to_dict(), dag.generate_causal_tests()), key=sort_test_dict),
+        )
+
+    def test_all_metamorphic_relations_implied_by_dag_parallel(self):
+        dag = CausalDAG(self.dag_dot_path, datatypes={v: float for v in {"X1", "X2", "X3", "Y", "Z", "M"}})
+        dag.add_edge("Z", "Y")  # Add a direct path from Z to Y so M becomes a mediator
+
+        expected_tests = []
+        for treatment, outcome in dag.edges:
+            expected_tests.append(
+                CausalTestCase(
+                    expected_causal_effect=SomeEffect(),
+                    effect_measure="coefficient",
+                    estimator=LinearRegressionEstimator(
+                        treatment_variable=treatment,
+                        outcome_variable=outcome,
+                        adjustment_set=dag.identification(treatment_variable=treatment, outcome_variable=outcome),
+                    ),
+                    name=f"{treatment} -> {outcome}",
+                    skip=False,
+                )
+            )
+        # We can't just do "nx.non_edges" here, since some independences are bidirectional (if there is no path from
+        # X -> ... -> Y) and some are unidirectional (if X -> Y is not in the DAG but X -> ... -> Y is).
+        for treatment, outcome in [
+            ("X1", "M"),
+            ("X1", "Y"),
+            ("X1", "X2"),
+            ("X2", "X1"),
+            ("X1", "X3"),
+            ("X3", "X1"),
+            ("Z", "X3"),
+            ("X3", "Z"),
+            ("X2", "M"),
+            ("X2", "Y"),
+            ("X3", "Y"),
+            ("X2", "X3"),
+            ("X3", "X2"),
+        ]:
+            expected_tests.append(
+                CausalTestCase(
+                    expected_causal_effect=NoEffect(),
+                    effect_measure="coefficient",
+                    estimator=LinearRegressionEstimator(
+                        treatment_variable=treatment,
+                        outcome_variable=outcome,
+                        adjustment_set=dag.identification(treatment_variable=treatment, outcome_variable=outcome),
+                    ),
+                    name=f"{treatment} _||_ {outcome}",
+                    skip=False,
+                )
+            )
+
+        self.assertEqual(
+            sorted(map(lambda t: t.to_dict(), expected_tests), key=sort_test_dict),
+            sorted(map(lambda t: t.to_dict(), dag.generate_causal_tests(threads=2)), key=sort_test_dict),
+        )
+
+    def test_all_metamorphic_relations_implied_by_dag_no_datatype(self):
+        causal_dag = CausalDAG(self.dag_dot_path)
+        with self.assertRaises(ValueError) as e:
+            causal_dag.generate_causal_tests()
+            self.assertEqual(e.exception, "No datatype specified for .")
+
+    def test_all_metamorphic_relations_implied_by_dag_ignore_cycles(self):
+        dcg = CausalDAG(self.dcg_dot_path, ignore_cycles=True, datatypes={v: float for v in {"a", "b", "c", "d"}})
+
+        expected_tests = [
+            CausalTestCase(
+                expected_causal_effect=SomeEffect(),
+                effect_measure="coefficient",
+                estimator=LinearRegressionEstimator(
+                    treatment_variable="a",
+                    outcome_variable="b",
+                    adjustment_set=dcg.identification(treatment_variable="a", outcome_variable="b"),
+                ),
+                name="a -> b",
+                skip=False,
+            )
+        ]
+        self.assertEqual(
+            sorted(map(lambda t: t.to_dict(), expected_tests), key=sort_test_dict),
+            sorted(map(lambda t: t.to_dict(), dcg.generate_causal_tests(threads=2)), key=sort_test_dict),
+        )
diff --git a/tests/specification_tests/test_variable.py b/tests/specification_tests/test_variable.py
deleted file mode 100644
index eabcf833..00000000
--- a/tests/specification_tests/test_variable.py
+++ /dev/null
@@ -1,29 +0,0 @@
-import unittest
-from enum import Enum
-from scipy.stats import norm, kstest
-
-from causal_testing.specification.variable import Variable, Input
-
-
-class TestVariable(unittest.TestCase):
-    """
-    Test the Variable class for basic methods.
-    """
-
-    def setUp(self) -> None:
-        pass
-
-    def test_typestring(self):
-        class Var(Variable):
-            pass
-
-        var = Var("v", int)
-        self.assertEqual(var.typestring(), "Var")
-
-    def test_copy(self):
-        ip = Input("ip", float, norm)
-        self.assertTrue(ip.copy() is not ip)
-        self.assertEqual(ip.copy().name, ip.name)
-        self.assertEqual(ip.copy().datatype, ip.datatype)
-        self.assertEqual(ip.copy().distribution, ip.distribution)
-        self.assertEqual(repr(ip), repr(ip.copy()))
diff --git a/tests/surrogate_tests/test_causal_surrogate_assisted.py b/tests/surrogate_tests/test_causal_surrogate_assisted.py
deleted file mode 100644
index 31a4bc04..00000000
--- a/tests/surrogate_tests/test_causal_surrogate_assisted.py
+++ /dev/null
@@ -1,238 +0,0 @@
-import os
-import shutil
-import tempfile
-import unittest
-import pandas as pd
-import numpy as np
-from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.scenario import Scenario
-from causal_testing.specification.variable import Input, Output
-from causal_testing.surrogate.causal_surrogate_assisted import (
-    SimulationResult,
-    CausalSurrogateAssistedTestCase,
-    Simulator,
-)
-from causal_testing.surrogate.surrogate_search_algorithms import GeneticSearchAlgorithm
-from causal_testing.estimation.cubic_spline_estimator import CubicSplineRegressionEstimator
-
-
-class TestSimulationResult(unittest.TestCase):
-
-    def setUp(self):
-        self.data = {"key": "value"}
-
-    def test_inputs(self):
-        fault_values = [True, False]
-
-        relationship_values = ["positive", "negative", None]
-
-        for fault in fault_values:
-
-            for relationship in relationship_values:
-                with self.subTest(fault=fault, relationship=relationship):
-                    result = SimulationResult(data=self.data, fault=fault, relationship=relationship)
-
-                    self.assertIsInstance(result.data, dict)
-
-                    self.assertEqual(result.fault, fault)
-
-                    self.assertEqual(result.relationship, relationship)
-
-
-class TestCausalSurrogate(unittest.TestCase):
-
-    @classmethod
-    def setUpClass(cls) -> None:
-        cls.class_df = load_class_df()
-
-    def setUp(self):
-        self.temp_dir_path = tempfile.mkdtemp()
-        self.dag_dot_path = os.path.join(self.temp_dir_path, "dag.dot")
-        dag_dot = """digraph DAG { rankdir=LR; Z -> X; X -> M [included=1, expected=positive]; M -> Y [included=1, expected=negative]; Z -> M; }"""
-        with open(self.dag_dot_path, "w") as f:
-            f.write(dag_dot)
-
-    def test_surrogate_model_generation(self):
-        causal_dag = CausalDAG(self.dag_dot_path)
-        z = Input("Z", int)
-        x = Input("X", float)
-        m = Input("M", int)
-        y = Output("Y", float)
-        scenario = Scenario(variables={z, x, m, y})
-
-        c_s_a_test_case = CausalSurrogateAssistedTestCase(scenario, causal_dag, None, None)
-        surrogate_models = c_s_a_test_case.generate_surrogates()
-        self.assertEqual(len(surrogate_models), 2)
-
-        for surrogate_model in surrogate_models:
-            self.assertIsInstance(surrogate_model, CubicSplineRegressionEstimator)
-            self.assertNotEqual(surrogate_model.base_test_case.treatment_variable.name, "Z")
-            self.assertNotEqual(surrogate_model.base_test_case.outcome_variable.name, "Z")
-
-    def test_causal_surrogate_assisted_execution(self):
-        df = self.class_df.copy()
-
-        causal_dag = CausalDAG(self.dag_dot_path)
-        z = Input("Z", int)
-        x = Input("X", float)
-        m = Input("M", int)
-        y = Output("Y", float)
-        scenario = Scenario(
-            variables={z, x, m, y}, constraints={"Z <= 0", "Z >= 3", "X <= 0", "X >= 3", "M <= 0", "M >= 3"}
-        )
-        search_algorithm = GeneticSearchAlgorithm(
-            config={
-                "parent_selection_type": "tournament",
-                "K_tournament": 4,
-                "mutation_type": "random",
-                "mutation_percent_genes": 50,
-                "mutation_by_replacement": True,
-            }
-        )
-        simulator = TestSimulator()
-
-        c_s_a_test_case = CausalSurrogateAssistedTestCase(scenario, causal_dag, search_algorithm, simulator)
-
-        result, iterations, result_data = c_s_a_test_case.execute(df)
-
-        self.assertIsInstance(result, SimulationResult)
-        self.assertEqual(iterations, 1)
-        self.assertEqual(len(result_data), 17)
-
-    def test_causal_surrogate_assisted_execution_failure(self):
-        df = self.class_df.copy()
-
-        causal_dag = CausalDAG(self.dag_dot_path)
-        z = Input("Z", int)
-        x = Input("X", float)
-        m = Input("M", int)
-        y = Output("Y", float)
-        scenario = Scenario(
-            variables={z, x, m, y}, constraints={"Z <= 0", "Z >= 3", "X <= 0", "X >= 3", "M <= 0", "M >= 3"}
-        )
-
-        search_algorithm = GeneticSearchAlgorithm(
-            config={
-                "parent_selection_type": "tournament",
-                "K_tournament": 4,
-                "mutation_type": "random",
-                "mutation_percent_genes": 50,
-                "mutation_by_replacement": True,
-            }
-        )
-        simulator = TestSimulatorFailing()
-
-        c_s_a_test_case = CausalSurrogateAssistedTestCase(scenario, causal_dag, search_algorithm, simulator)
-
-        result, iterations, result_data = c_s_a_test_case.execute(df, 1)
-
-        self.assertIsInstance(result, str)
-        self.assertEqual(iterations, 1)
-        self.assertEqual(len(result_data), 17)
-
-    def test_causal_surrogate_assisted_execution_custom_aggregator(self):
-        df = self.class_df.copy()
-
-        causal_dag = CausalDAG(self.dag_dot_path)
-        z = Input("Z", int)
-        x = Input("X", float)
-        m = Input("M", int)
-        y = Output("Y", float)
-        scenario = Scenario(
-            variables={z, x, m, y}, constraints={"Z <= 0", "Z >= 3", "X <= 0", "X >= 3", "M <= 0", "M >= 3"}
-        )
-
-        search_algorithm = GeneticSearchAlgorithm(
-            config={
-                "parent_selection_type": "tournament",
-                "K_tournament": 4,
-                "mutation_type": "random",
-                "mutation_percent_genes": 50,
-                "mutation_by_replacement": True,
-            }
-        )
-        simulator = TestSimulator()
-
-        c_s_a_test_case = CausalSurrogateAssistedTestCase(scenario, causal_dag, search_algorithm, simulator)
-
-        result, iterations, result_data = c_s_a_test_case.execute(df, custom_data_aggregator=data_double_aggregator)
-
-        self.assertIsInstance(result, SimulationResult)
-        self.assertEqual(iterations, 1)
-        self.assertEqual(len(result_data), 18)
-
-    def test_causal_surrogate_assisted_execution_incorrect_search_config(self):
-        df = self.class_df.copy()
-
-        causal_dag = CausalDAG(self.dag_dot_path)
-        z = Input("Z", int)
-        x = Input("X", float)
-        m = Input("M", int)
-        y = Output("Y", float)
-        scenario = Scenario(
-            variables={z, x, m, y}, constraints={"Z <= 0", "Z >= 3", "X <= 0", "X >= 3", "M <= 0", "M >= 3"}
-        )
-
-        search_algorithm = GeneticSearchAlgorithm(
-            config={
-                "parent_selection_type": "tournament",
-                "K_tournament": 4,
-                "mutation_type": "random",
-                "mutation_percent_genes": 50,
-                "mutation_by_replacement": True,
-                "gene_space": "Something",
-            }
-        )
-        simulator = TestSimulator()
-
-        c_s_a_test_case = CausalSurrogateAssistedTestCase(scenario, causal_dag, search_algorithm, simulator)
-
-        self.assertRaises(
-            ValueError,
-            c_s_a_test_case.execute,
-            df=df,
-            custom_data_aggregator=data_double_aggregator,
-        )
-
-    def tearDown(self) -> None:
-        shutil.rmtree(self.temp_dir_path)
-
-
-def load_class_df():
-    """Get the testing data and put into a dataframe."""
-
-    class_df = pd.DataFrame(
-        {"Z": np.arange(16), "X": np.arange(16), "M": np.arange(16, 32), "Y": np.arange(32, 16, -1)}
-    )
-    return class_df
-
-
-class TestSimulator(Simulator):
-
-    def run_with_config(self, configuration: dict) -> SimulationResult:
-        return SimulationResult({"Z": 1, "X": 1, "M": 1, "Y": 1}, True, None)
-
-    def startup(self):
-        pass
-
-    def shutdown(self):
-        pass
-
-
-class TestSimulatorFailing(Simulator):
-
-    def run_with_config(self, configuration: dict) -> SimulationResult:
-        return SimulationResult({"Z": 1, "X": 1, "M": 1, "Y": 1}, False, None)
-
-    def startup(self):
-        pass
-
-    def shutdown(self):
-        pass
-
-
-def data_double_aggregator(data, new_data):
-    """Previously used data.append(new_data), however, pandas version >2 requires pd.concat() since append is now a private method.
-    Converting new_data to a pd.DataFrame is required to use pd.concat()."""
-    new_data = pd.DataFrame([new_data])
-    return pd.concat([data, new_data, new_data], ignore_index=True)
diff --git a/tests/testing_tests/test_causal_effect.py b/tests/testing_tests/test_causal_effect.py
index c0293c00..aa910045 100644
--- a/tests/testing_tests/test_causal_effect.py
+++ b/tests/testing_tests/test_causal_effect.py
@@ -1,209 +1,161 @@
 import unittest
+
 import pandas as pd
-from causal_testing.testing.causal_effect import ExactValue, SomeEffect, Positive, Negative, NoEffect
-from causal_testing.testing.causal_test_result import CausalTestResult
-from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
+
 from causal_testing.estimation.effect_estimate import EffectEstimate
+from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
+from causal_testing.testing.causal_effect import ExactValue, Negative, NoEffect, Positive, SomeEffect
 from causal_testing.utils.validation import CausalValidator
-from causal_testing.testing.base_test_case import BaseTestCase
-from causal_testing.specification.variable import Input, Output
 
 
 class TestCausalEffect(unittest.TestCase):
     """Test the TestCausalEffect basic methods."""
 
     def setUp(self) -> None:
-        base_test_case = BaseTestCase(Input("A", float), Output("A", float))
         self.estimator = LinearRegressionEstimator(
-            base_test_case=base_test_case,
-            treatment_value=1,
-            control_value=0,
-            adjustment_set={},
+            treatment_variable="A", outcome_variable="B", treatment_value=1, control_value=0, adjustment_set=set()
         )
 
-    def test_None_ci(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series(0)),
+    def test_effect_estimate_to_dict(self):
+        effect_estimate = EffectEstimate(
+            type="ate", value=pd.Series({"A": 1}), ci_low=pd.Series({"A": 0.1}), ci_high=pd.Series({"A": 1.2})
         )
-
-        self.assertIsNone(ctr.effect_estimate.ci_low)
-        self.assertIsNone(ctr.effect_estimate.ci_high)
-
-    def test_empty_adjustment_set(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series(0)),
+        self.assertEqual(
+            effect_estimate.to_dict(),
+            {"effect_measure": "ate", "effect_estimate": {"A": 1}, "ci_low": {"A": 0.1}, "ci_high": {"A": 1.2}},
         )
 
-        self.assertIsNone(ctr.effect_estimate.ci_low)
-        self.assertIsNone(ctr.effect_estimate.ci_high)
+    def test_effect_estimate_to_dict_no_ci(self):
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series({"A": 1}))
+        self.assertEqual(
+            effect_estimate.to_dict(),
+            {"effect_measure": "ate", "effect_estimate": {"A": 1}},
+        )
 
     def test_Positive_ate_pass(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="ate", value=pd.Series(5.05), ci_low=pd.Series(5), ci_high=pd.Series(6)
-            ),
-        )
-        ev = Positive()
-        self.assertTrue(ev.apply(ctr))
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series(5.05), ci_low=pd.Series(5), ci_high=pd.Series(6))
+        self.assertTrue(Positive().apply(effect_estimate))
 
     def test_Positive_risk_ratio_pass(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="risk_ratio", value=pd.Series(5.05), ci_low=pd.Series(5), ci_high=pd.Series(6)
-            ),
+        effect_estimate = EffectEstimate(
+            type="risk_ratio", value=pd.Series(5.05), ci_low=pd.Series(5), ci_high=pd.Series(6)
         )
-        ev = Positive()
-        self.assertTrue(ev.apply(ctr))
+        self.assertTrue(Positive().apply(effect_estimate))
 
     def test_Positive_fail(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series(0), ci_low=pd.Series(-1), ci_high=pd.Series(1)),
-        )
-        ev = Positive()
-        self.assertFalse(ev.apply(ctr))
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series(0), ci_low=pd.Series(-1), ci_high=pd.Series(1))
+        self.assertFalse(Positive().apply(effect_estimate))
 
     def test_Negative_ate_pass(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="ate", value=pd.Series(-5.05), ci_low=pd.Series(-6), ci_high=pd.Series(-5)
-            ),
+        effect_estimate = EffectEstimate(
+            type="ate", value=pd.Series(-5.05), ci_low=pd.Series(-6), ci_high=pd.Series(-5)
         )
-        ev = Negative()
-        self.assertTrue(ev.apply(ctr))
+        self.assertTrue(Negative().apply(effect_estimate))
 
     def test_Negative_risk_ratio_pass(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="risk_ratio", value=pd.Series(0.2), ci_low=pd.Series(0.1), ci_high=pd.Series(0.5)
-            ),
+        effect_estimate = EffectEstimate(
+            type="risk_ratio", value=pd.Series(0.2), ci_low=pd.Series(0.1), ci_high=pd.Series(0.5)
         )
-        ev = Negative()
-        self.assertTrue(ev.apply(ctr))
+        self.assertTrue(Negative().apply(effect_estimate))
 
     def test_Negative_fail(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series(0), ci_low=pd.Series(-1), ci_high=pd.Series(1)),
-        )
-        ev = Negative()
-        self.assertFalse(ev.apply(ctr))
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series(0), ci_low=pd.Series(-1), ci_high=pd.Series(1))
+        self.assertFalse(Negative().apply(effect_estimate))
 
     def test_exactValue_pass(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series(5.05)),
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series(5.05))
+        self.assertTrue(ExactValue(value=5, atol=0.1).apply(effect_estimate))
+
+    def test_exactValue_to_dict(self):
+        self.assertTrue(
+            ExactValue(value=5.01, ci_low=5.0, ci_high=5.08).to_dict(),
+            {"name": "ExactValue", "effect_type": "direct", "value": 5, "ci_high": 5.0, "ci_low": 5.08},
         )
-        ev = ExactValue(5, 0.1)
-        self.assertTrue(ev.apply(ctr))
 
-    def test_exactValue_pass_ci(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="ate", value=pd.Series(5.05), ci_low=pd.Series(4), ci_high=pd.Series(6)
-            ),
+    def test_exactValue_categorical_pass(self):
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series({"color[T.red]": 5.05, "color[T.blue]": 4.03}))
+        self.assertTrue(
+            ExactValue(value=pd.Series({"color[T.red]": 5, "color[T.blue]": 4}), atol=0.1).apply(effect_estimate)
         )
-        ev = ExactValue(5, 0.1)
-        self.assertTrue(ev.apply(ctr))
+
+    def test_exactValue_pass_ci(self):
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series(5.05), ci_low=pd.Series(4), ci_high=pd.Series(6))
+        self.assertTrue(ExactValue(value=5, atol=0.1).apply(effect_estimate))
 
     def test_exactValue_ci_pass_ci(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="ate", value=pd.Series(5.05), ci_low=pd.Series(4.1), ci_high=pd.Series(5.9)
-            ),
+        effect_estimate = EffectEstimate(
+            type="ate", value=pd.Series(5.05), ci_low=pd.Series(4.1), ci_high=pd.Series(5.9)
         )
-        ev = ExactValue(5, ci_low=4, ci_high=6)
-        self.assertTrue(ev.apply(ctr))
+        self.assertTrue(ExactValue(value=5, atol=0.05, ci_low=4, ci_high=6).apply(effect_estimate))
 
     def test_exactValue_ci_fail_ci(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="ate", value=pd.Series(5.05), ci_low=pd.Series(3.9), ci_high=pd.Series(6.1)
-            ),
+        effect_estimate = EffectEstimate(
+            type="ate", value=pd.Series(5.05), ci_low=pd.Series(4.1), ci_high=pd.Series(5.9)
         )
-        ev = ExactValue(5, ci_low=4, ci_high=6)
-        self.assertFalse(ev.apply(ctr))
+        self.assertFalse(ExactValue(value=5, atol=0.04, ci_low=4, ci_high=6).apply(effect_estimate))
 
     def test_exactValue_fail(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series(0)),
-        )
-        ev = ExactValue(5, 0.1)
-        self.assertFalse(ev.apply(ctr))
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series(0))
+        self.assertFalse(ExactValue(value=5, atol=0.1).apply(effect_estimate))
 
     def test_invalid_atol(self):
         with self.assertRaises(ValueError):
-            ExactValue(5, -0.1)
+            ExactValue(value=5, atol=-0.1)
 
     def test_unspecified_ci_high(self):
         with self.assertRaises(ValueError):
-            ExactValue(5, ci_low=-0.1)
+            ExactValue(value=5, ci_low=-0.1)
 
     def test_unspecified_ci_low(self):
         with self.assertRaises(ValueError):
-            ExactValue(5, ci_high=-0.1)
+            ExactValue(value=5, ci_high=-0.1)
 
     def test_invalid_ci_range(self):
         with self.assertRaises(ValueError):
-            ExactValue(5, ci_low=6, ci_high=7, atol=0.05)
+            ExactValue(value=5, ci_low=6, ci_high=7, atol=0.05)
 
     def test_invalid_ci_atol(self):
         with self.assertRaises(ValueError):
-            ExactValue(1000, ci_low=999, ci_high=1001, atol=50)
+            ExactValue(value=1000, ci_low=999, ci_high=1001, atol=50)
 
     def test_invalid(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="invalid", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
-            ),
+        effect_estimate = EffectEstimate(
+            type="invalid", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
         )
         with self.assertRaises(ValueError):
-            SomeEffect().apply(ctr)
+            SomeEffect().apply(effect_estimate)
         with self.assertRaises(ValueError):
-            NoEffect().apply(ctr)
+            NoEffect().apply(effect_estimate)
         with self.assertRaises(ValueError):
-            Positive().apply(ctr)
+            Positive().apply(effect_estimate)
         with self.assertRaises(ValueError):
-            Negative().apply(ctr)
+            Negative().apply(effect_estimate)
 
     def test_someEffect_pass_coefficient(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="coefficient", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
-            ),
+        effect_estimate = EffectEstimate(
+            type="coefficient", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
         )
-        self.assertTrue(SomeEffect().apply(ctr))
-        self.assertFalse(NoEffect().apply(ctr))
+        self.assertTrue(SomeEffect().apply(effect_estimate))
+        self.assertFalse(NoEffect().apply(effect_estimate))
 
     def test_someEffect_pass_ate(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="coefficient", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
-            ),
+        effect_estimate = EffectEstimate(
+            type="coefficient", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
         )
-        self.assertTrue(SomeEffect().apply(ctr))
-        self.assertFalse(NoEffect().apply(ctr))
+        self.assertTrue(SomeEffect().apply(effect_estimate))
+        self.assertFalse(NoEffect().apply(effect_estimate))
 
     def test_someEffect_pass_rr(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="coefficient", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
-            ),
+        effect_estimate = EffectEstimate(
+            type="coefficient", value=pd.Series(5.05), ci_low=pd.Series(4.8), ci_high=pd.Series(6.7)
         )
-        self.assertTrue(SomeEffect().apply(ctr))
-        self.assertFalse(NoEffect().apply(ctr))
+        self.assertTrue(SomeEffect().apply(effect_estimate))
+        self.assertFalse(NoEffect().apply(effect_estimate))
 
     def test_someEffect_fail(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(
-                type="ate", value=pd.Series(0), ci_low=pd.Series(-0.1), ci_high=pd.Series(0.2)
-            ),
-        )
-        self.assertFalse(SomeEffect().apply(ctr))
-        self.assertTrue(NoEffect().apply(ctr))
-
-    def test_someEffect_None(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series(0)),
-        )
-        self.assertEqual(SomeEffect().apply(ctr), None)
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series(0), ci_low=pd.Series(-0.1), ci_high=pd.Series(0.2))
+        self.assertFalse(SomeEffect().apply(effect_estimate))
+        self.assertTrue(NoEffect().apply(effect_estimate))
 
     def test_positive_risk_ratio_e_value(self):
         cv = CausalValidator()
@@ -226,10 +178,8 @@ def test_negative_risk_ratio_e_value_using_ci(self):
         self.assertEqual(round(e_value, 4), 1.4625)
 
     def test_multiple_value_exception_caught(self):
-        ctr = CausalTestResult(
-            effect_estimate=EffectEstimate(type="ate", value=pd.Series([0, 1])),
-        )
+        effect_estimate = EffectEstimate(type="ate", value=pd.Series([0, 1]))
         with self.assertRaises(ValueError):
-            Positive().apply(ctr)
+            Positive().apply(effect_estimate)
         with self.assertRaises(ValueError):
-            Negative().apply(ctr)
+            Negative().apply(effect_estimate)
diff --git a/tests/testing_tests/test_causal_test_adequacy.py b/tests/testing_tests/test_causal_test_adequacy.py
index 36ed256a..2aec4a40 100644
--- a/tests/testing_tests/test_causal_test_adequacy.py
+++ b/tests/testing_tests/test_causal_test_adequacy.py
@@ -1,18 +1,16 @@
 import os
 import unittest
-import scipy
+
 import pandas as pd
+import scipy
 
-from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
 from causal_testing.estimation.ipcw_estimator import IPCWEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
+from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
+from causal_testing.specification.causal_dag import CausalDAG
+from causal_testing.testing.causal_effect import NoEffect, SomeEffect
 from causal_testing.testing.causal_test_case import CausalTestCase
 from causal_testing.testing.dag_adequacy import DAGAdequacy
-from causal_testing.testing.causal_effect import NoEffect, SomeEffect
-from causal_testing.specification.scenario import Scenario
 from causal_testing.testing.data_adequacy import DataAdequacy
-from causal_testing.specification.variable import Input, Output
-from causal_testing.specification.causal_dag import CausalDAG
 
 
 class TestCausalTestAdequacy(unittest.TestCase):
@@ -25,24 +23,14 @@ def setUp(self) -> None:
         self.df = pd.read_csv("tests/resources/data/data_with_categorical.csv")
         self.dag = CausalDAG("tests/resources/data/dag.dot")
         self.example_distribution = scipy.stats.uniform(1, 10)
-        inputs = [
-            Input("test_input", float, self.example_distribution),
-            Input("test_input_no_dist", float, self.example_distribution),
-        ]
-        outputs = [Output("test_output", float)]
-        self.scenario = Scenario(variables=inputs + outputs)
 
     def test_data_adequacy_numeric(self):
-        base_test_case = BaseTestCase(
-            Input("test_input", float, self.example_distribution), Output("test_output", float)
-        )
         estimator = LinearRegressionEstimator(
-            base_test_case=base_test_case, treatment_value=None, control_value=None, adjustment_set={}
+            treatment_variable="test_input", outcome_variable="test_output", adjustment_set=set()
         )
         causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
-            expected_causal_effect=NoEffect(),
-            estimate_type="coefficient",
+            expected_causal_effect=NoEffect(atol=1e-10),
+            effect_measure="coefficient",
             estimator=estimator,
         )
         adequacy_metric = causal_test_case.measure_adequacy(self.df)
@@ -53,19 +41,16 @@ def test_data_adequacy_numeric(self):
             delta=1.0,
             msg=f"Expected kurtosis near 0, got {adequacy_metric.kurtosis['test_input']}",
         )  # This adds a numerical tolerance for Pandas
-        self.assertEqual(adequacy_metric.passing, 19, f"Expected passing 19 not {adequacy_metric.passing}")
+        self.assertEqual(adequacy_metric.passing, 100, f"Expected passing 100 not {adequacy_metric.passing}")
         self.assertEqual(adequacy_metric.successful, 100, f"Expected successful 100 not {adequacy_metric.successful}")
 
     def test_data_adequacy_categorical(self):
-        base_test_case = BaseTestCase(
-            Input("test_input_no_dist", float, self.example_distribution), Output("test_output", float)
-        )
-        estimator = LinearRegressionEstimator(base_test_case=base_test_case, adjustment_set={})
         causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
-            expected_causal_effect=NoEffect(),
-            estimate_type="coefficient",
-            estimator=estimator,
+            expected_causal_effect=NoEffect(atol=1e-10),
+            effect_measure="coefficient",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="test_input_no_dist", outcome_variable="test_output", adjustment_set=set()
+            ),
         )
         adequacy_metric = causal_test_case.measure_adequacy(self.df)
 
@@ -78,11 +63,41 @@ def test_data_adequacy_categorical(self):
         self.assertEqual(adequacy_metric.passing, 100, f"Expected passing 100 not {adequacy_metric.passing}")
         self.assertEqual(adequacy_metric.successful, 100, f"Expected successful 100 not {adequacy_metric.successful}")
 
+    def test_data_adequacy_categorical_inestimable(self):
+        df = pd.read_csv("tests/resources/data/scarf_data.csv")
+        causal_test_case = CausalTestCase(
+            expected_causal_effect=NoEffect(atol=1e-10),
+            effect_measure="coefficient",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="color", outcome_variable="completed", adjustment_set=set()
+            ),
+        )
+        adequacy_metric = causal_test_case.measure_adequacy(df.loc[df["color"] == "grey"])
+
+        self.assertEqual(adequacy_metric.kurtosis, None, f"Expected passing None not {adequacy_metric.kurtosis}")
+        self.assertEqual(adequacy_metric.passing, 0, f"Expected passing 0 not {adequacy_metric.passing}")
+        self.assertEqual(adequacy_metric.successful, 0, f"Expected successful 0 not {adequacy_metric.successful}")
+        self.assertEqual(adequacy_metric.results, [])
+
+    def test_data_adequacy_categorical_partly_inestimable(self):
+        df = pd.read_csv("tests/resources/data/scarf_data.csv")
+        causal_test_case = CausalTestCase(
+            expected_causal_effect=NoEffect(atol=1e-10),
+            effect_measure="coefficient",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="color", outcome_variable="completed", adjustment_set=set()
+            ),
+        )
+        adequacy_metric = causal_test_case.measure_adequacy(df.loc[df["length_in"] == 55])
+
+        self.assertEqual(adequacy_metric.kurtosis.values, [0], f"Expected [0] not {adequacy_metric.kurtosis.values}")
+        self.assertEqual(adequacy_metric.passing, 63, f"Expected passing 63 not {adequacy_metric.passing}")
+        self.assertEqual(adequacy_metric.successful, 63, f"Expected successful 63 not {adequacy_metric.successful}")
+
     def test_data_adequacy_group_by(self):
         timesteps_per_intervention = 1
         control_strategy = [[t, "t", 0] for t in range(1, 4, timesteps_per_intervention)]
         treatment_strategy = [[t, "t", 1] for t in range(1, 4, timesteps_per_intervention)]
-        outcome = Output("outcome", float)
         fit_bl_switch_formula = "xo_t_do ~ time"
         df = pd.read_csv("tests/resources/data/temporal_data.csv")
         df["ok"] = df["outcome"] == 1
@@ -90,40 +105,121 @@ def test_data_adequacy_group_by(self):
             timesteps_per_observation=timesteps_per_intervention,
             control_strategy=control_strategy,
             treatment_strategy=treatment_strategy,
-            outcome=outcome,
+            outcome_variable="outcome",
             status_column="ok",
             fit_bl_switch_formula=fit_bl_switch_formula,
             fit_bltd_switch_formula=fit_bl_switch_formula,
             eligibility=None,
         )
-        base_test_case = BaseTestCase(Input("t", float), Output("outcome", float))
 
         causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
             expected_causal_effect=SomeEffect(),
-            estimate_type="hazard_ratio",
+            effect_measure="hazard_ratio",
             estimator=estimation_model,
         )
         adequacy_metric = causal_test_case.measure_adequacy(df, group_by="id")
 
         self.assertEqual(
             round(adequacy_metric.kurtosis["trtrand"], 3),
-            -2.739,
+            -0.857,
             f"Expected kurtosis not {round(adequacy_metric.kurtosis['trtrand'], 3)}",
         )
-        self.assertEqual(adequacy_metric.passing, 1, f"Expected passing 1 not {adequacy_metric.passing}")
-        self.assertEqual(adequacy_metric.successful, 5, f"Expected successful 5 not {adequacy_metric.successful}")
+        self.assertEqual(adequacy_metric.passing, 32, f"Expected passing 32 not {adequacy_metric.passing}")
+        self.assertEqual(adequacy_metric.successful, 100, f"Expected successful 100 not {adequacy_metric.successful}")
 
-    def test_dag_adequacy_dependent(self):
-        base_test_case = BaseTestCase(
-            treatment_variable="test_input",
-            outcome_variable="B",
-            effect=None,
+    def test_to_dict(self):
+        estimator = LinearRegressionEstimator(
+            treatment_variable="test_input", outcome_variable="test_output", adjustment_set=set()
         )
         causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
+            expected_causal_effect=NoEffect(atol=1e-10),
+            effect_measure="coefficient",
+            estimator=estimator,
+        )
+        adequacy_metric = causal_test_case.measure_adequacy(self.df, bootstrap_size=10)
+
+        expected_dict = {
+            "kurtosis": {"test_input": 0.0},
+            "passing": 10,
+            "successful": 10,
+            "bootstrap_size": 10,
+            "results": {
+                "effect_estimate": {
+                    0: -2.220446049250313e-16,
+                    1: -1.1102230246251565e-16,
+                    2: 7.632783294297951e-17,
+                    3: 5.551115123125783e-17,
+                    4: 6.938893903907228e-17,
+                    5: 5.551115123125783e-17,
+                    6: 4.163336342344337e-17,
+                    7: -1.6653345369377348e-16,
+                    8: 3.469446951953614e-17,
+                    9: -1.3877787807814457e-16,
+                },
+                "ci_low": {
+                    0: -7.771553129157294e-16,
+                    1: -2.279269223868238e-16,
+                    2: -1.8906584687236155e-16,
+                    3: -1.57411847966069e-16,
+                    4: -3.27696708597398e-17,
+                    5: 2.3327440223904727e-17,
+                    6: -6.643195454332691e-19,
+                    7: -3.158788196765981e-16,
+                    8: -1.878128765814627e-16,
+                    9: -3.348328858540416e-16,
+                },
+                "ci_high": {
+                    0: 3.3306610306566683e-16,
+                    1: 5.8823174617924694e-18,
+                    2: 3.4172151275832057e-16,
+                    3: 2.6843415042858463e-16,
+                    4: 1.7154754893788438e-16,
+                    5: 8.769486223861093e-17,
+                    6: 8.393104639232001e-17,
+                    7: -1.718808771094884e-17,
+                    8: 2.57201815620535e-16,
+                    9: 5.727712969775247e-17,
+                },
+                "test_index": {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5, 6: 6, 7: 7, 8: 8, 9: 9},
+                "passed": {
+                    0: True,
+                    1: True,
+                    2: True,
+                    3: True,
+                    4: True,
+                    5: True,
+                    6: True,
+                    7: True,
+                    8: True,
+                    9: True,
+                },
+                "var": {
+                    0: "test_input",
+                    1: "test_input",
+                    2: "test_input",
+                    3: "test_input",
+                    4: "test_input",
+                    5: "test_input",
+                    6: "test_input",
+                    7: "test_input",
+                    8: "test_input",
+                    9: "test_input",
+                },
+            },
+        }
+        # Use json_normalize to avoid rounding errors
+        pd.testing.assert_frame_equal(
+            pd.json_normalize(expected_dict).round(2),
+            pd.json_normalize(adequacy_metric.to_dict(include_results=True)).round(2),
+        )
+
+    def test_dag_adequacy_dependent(self):
+        causal_test_case = CausalTestCase(
+            estimator=LinearRegressionEstimator(
+                treatment_variable="test_input", outcome_variable="B", adjustment_set=set()
+            ),
             expected_causal_effect=None,
-            estimate_type=None,
+            effect_measure=None,
         )
         test_suite = [causal_test_case]
         dag_adequacy = DAGAdequacy(self.dag, test_suite)
@@ -162,15 +258,12 @@ def test_dag_adequacy_dependent(self):
         )
 
     def test_dag_adequacy_independent(self):
-        base_test_case = BaseTestCase(
-            treatment_variable="test_input",
-            outcome_variable="C",
-            effect=None,
-        )
         causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
+            estimator=LinearRegressionEstimator(
+                treatment_variable="test_input", outcome_variable="C", adjustment_set=set()
+            ),
             expected_causal_effect=None,
-            estimate_type=None,
+            effect_measure=None,
         )
         test_suite = [causal_test_case]
         dag_adequacy = DAGAdequacy(self.dag, test_suite)
@@ -209,15 +302,12 @@ def test_dag_adequacy_independent(self):
         )
 
     def test_dag_adequacy_independent_other_way(self):
-        base_test_case = BaseTestCase(
-            treatment_variable="C",
-            outcome_variable="test_input",
-            effect=None,
-        )
         causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
+            estimator=LinearRegressionEstimator(
+                treatment_variable="C", outcome_variable="test_input", adjustment_set=set()
+            ),
             expected_causal_effect=None,
-            estimate_type=None,
+            effect_measure=None,
         )
         test_suite = [causal_test_case]
         dag_adequacy = DAGAdequacy(self.dag, test_suite)
diff --git a/tests/testing_tests/test_causal_test_case.py b/tests/testing_tests/test_causal_test_case.py
index 53bf0b4e..7908001e 100644
--- a/tests/testing_tests/test_causal_test_case.py
+++ b/tests/testing_tests/test_causal_test_case.py
@@ -1,209 +1,132 @@
-import unittest
 import os
-import tempfile
 import shutil
-import pandas as pd
+import tempfile
+import unittest
+
 import numpy as np
+import pandas as pd
 
-from causal_testing.specification.scenario import Scenario
-from causal_testing.specification.variable import Input, Output
+from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
 from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.testing.causal_test_case import CausalTestCase
 from causal_testing.testing.causal_effect import ExactValue
-from causal_testing.estimation.linear_regression_estimator import LinearRegressionEstimator
-from causal_testing.testing.base_test_case import BaseTestCase
+from causal_testing.testing.causal_test_case import CausalTestCase
 
 
 class TestCausalTestCase(unittest.TestCase):
-    """Test the CausalTestCase class.
-
-    The base test case is a data class which contains the minimum information
-    necessary to perform identification. The CausalTestCase class represents
-    a causal test case. We here test the basic getter methods.
-    """
-
-    def setUp(self) -> None:
-        # 2. Create Scenario and Causal Specification
-        A = Input("A", float)
-        C = Output("C", float)
-
-        # 3. Create an intervention and causal test case
-        self.expected_causal_effect = ExactValue(4)
-        self.base_test_case = BaseTestCase(A, C)
-        self.causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case,
-            expected_causal_effect=self.expected_causal_effect,
-            estimator=LinearRegressionEstimator(
-                base_test_case=self.base_test_case,
-                adjustment_set=set(),
-                control_value=0,
-                treatment_value=1,
-            ),
-        )
-
-    def test_str(self):
-        print(str(self.causal_test_case))
-        self.assertEqual(
-            str(self.causal_test_case),
-            "Running {'A': 1} instead of {'A': 0} should cause the following changes to {'C'}: ExactValue: 4±0.2.",
-        )
-
-
-class TestCausalTestExecution(unittest.TestCase):
     """
     Test the causal test execution workflow using observational data.
     """
 
     def setUp(self) -> None:
-        # 1. Create Causal DAG
-        self.temp_dir_path = tempfile.mkdtemp()
-        dag_dot_path = os.path.join(self.temp_dir_path, "dag.dot")
-        dag_dot = """digraph G { A -> C; D -> A; D -> C}"""
-        with open(dag_dot_path, "w") as file:
-            file.write(dag_dot)
-        self.causal_dag = CausalDAG(dag_dot_path)
-
-        # 2. Create Scenario and Causal Specification
-        self.A = Input("A", float)
-        self.C = Output("C", float)
-        self.D = Output("D", float)
-        self.scenario = Scenario({self.A, self.C, self.D})
-
-        # 3. Create a causal test case
+        # Create Causal DAG
+        self.causal_dag = CausalDAG()
+        self.causal_dag.add_edges_from([("A", "C"), ("D", "A"), ("D", "C")])
+
+        # Create a causal test case
         self.expected_causal_effect = ExactValue(4)
-        self.base_test_case_A_C = BaseTestCase(self.A, self.C)
-        self.base_test_case_D_A = BaseTestCase(self.D, self.A)
+        self.minimal_adjustment_set = self.causal_dag.identification(treatment_variable="A", outcome_variable="C")
+        self.treatment_value = 1
+        self.control_value = 0
+        self.estimator = LinearRegressionEstimator(
+            treatment_variable="A",
+            outcome_variable="C",
+            treatment_value=self.treatment_value,
+            control_value=self.control_value,
+            adjustment_set=self.minimal_adjustment_set,
+        )
         self.causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case_A_C,
+            estimator=self.estimator,
             expected_causal_effect=self.expected_causal_effect,
-            # control_value=0,
-            # treatment_value=1,
+            effect_measure="ate",
         )
 
-        # 4. Create dummy test data and write to csv
+        # Create dummy test data
         np.random.seed(1)
         self.df = pd.DataFrame({"D": list(np.random.normal(60, 10, 1000))})  # D = exogenous
         self.df["A"] = [1 if d > 50 else 0 for d in self.df["D"]]
         self.df["C"] = self.df["D"] + (4 * (self.df["A"] + 2))  # C = (4*(A+2)) + D
-        # self.observational_data_csv_path = os.path.join(self.temp_dir_path, "observational_data.csv")
-        # self.df.to_csv(self.observational_data_csv_path, index=False)
-
-        # 5. Create minimal adjustment set
-        self.minimal_adjustment_set = self.causal_dag.identification(self.base_test_case_A_C)
-        # 6. Easier to access treatment and outcome values
-        self.treatment_value = 1
-        self.control_value = 0
 
-    def tearDown(self) -> None:
-        shutil.rmtree(self.temp_dir_path)
+    def test_treatment_no_estimator(self):
+        self.assertEqual(
+            CausalTestCase(expected_causal_effect=self.expected_causal_effect, effect_measure="ate").treatment_variable,
+            None,
+        )
 
-    def test_invalid_base_test_case(self):
-        with self.assertRaises(ValueError):
-            BaseTestCase(self.A, self.A)
+    def test_outcome_no_estimator(self):
+        self.assertEqual(
+            CausalTestCase(expected_causal_effect=self.expected_causal_effect, effect_measure="ate").outcome_variable,
+            None,
+        )
 
     def test_check_minimum_adjustment_set(self):
         """Check that the minimum adjustment set is correctly made"""
-        minimal_adjustment_set = self.causal_dag.identification(self.base_test_case_A_C)
-        self.assertEqual(minimal_adjustment_set, {"D"})
-
-    def test_invalid_causal_effect(self):
-        """Check that executing the causal test case returns the correct results for dummy data using a linear
-        regression estimator."""
-        base_test_case = BaseTestCase(treatment_variable=self.A, outcome_variable=self.C, effect="error")
-
-        with self.assertRaises(Exception):
-            self.causal_dag.identification(base_test_case)
+        self.assertEqual(self.minimal_adjustment_set, {"D"})
 
     def test_execute_test_observational_linear_regression_estimator(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
         regression estimator."""
-        estimation_model = LinearRegressionEstimator(
-            base_test_case=self.base_test_case_A_C,
-            treatment_value=self.treatment_value,
-            control_value=self.control_value,
-            adjustment_set=self.minimal_adjustment_set,
-        )
+
         causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case_A_C,
             expected_causal_effect=self.expected_causal_effect,
-            estimator=estimation_model,
+            estimator=self.estimator,
+            effect_measure="ate",
         )
-        causal_test_result = causal_test_case.estimate_effect(self.df)
-        pd.testing.assert_series_equal(causal_test_result.effect_estimate.value, pd.Series(4.0), atol=1e-10)
+        effect_estimate = causal_test_case.estimate_effect(self.df)
+        pd.testing.assert_series_equal(effect_estimate.value, pd.Series(4.0), atol=1e-10)
 
     def test_execute_test_observational_linear_regression_estimator_direct_effect(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
         regression estimator."""
-        base_test_case = BaseTestCase(treatment_variable=self.A, outcome_variable=self.C, effect="direct")
-        estimation_model = LinearRegressionEstimator(
-            base_test_case=self.base_test_case_A_C,
-            treatment_value=self.treatment_value,
-            control_value=self.control_value,
-            adjustment_set=self.causal_dag.identification(base_test_case),
-        )
-
-        causal_test_case = CausalTestCase(
-            base_test_case=base_test_case,
-            expected_causal_effect=self.expected_causal_effect,
-            estimator=estimation_model,
-        )
-
-        # 6. Easier to access treatment and outcome values
-        self.treatment_value = 1
-        self.control_value = 0
-        causal_test_result = causal_test_case.estimate_effect(self.df)
-        pd.testing.assert_series_equal(causal_test_result.effect_estimate.value, pd.Series(4.0), atol=1e-10)
+        effect_estimate = self.causal_test_case.estimate_effect(self.df)
+        pd.testing.assert_series_equal(effect_estimate.value, pd.Series(4.0), atol=1e-10)
 
     def test_execute_test_observational_linear_regression_estimator_coefficient(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
         regression estimator."""
-        estimation_model = LinearRegressionEstimator(
-            base_test_case=self.base_test_case_D_A,
-            treatment_value=self.treatment_value,
-            control_value=self.control_value,
-            adjustment_set=self.minimal_adjustment_set,
-        )
         causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case_A_C,
             expected_causal_effect=self.expected_causal_effect,
-            estimator=estimation_model,
-            estimate_type="coefficient",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="D",
+                outcome_variable="A",
+                treatment_value=self.treatment_value,
+                control_value=self.control_value,
+                adjustment_set=self.causal_dag.identification(treatment_variable="D", outcome_variable="A"),
+            ),
+            effect_measure="coefficient",
         )
-        causal_test_result = causal_test_case.estimate_effect(self.df)
-        pd.testing.assert_series_equal(causal_test_result.effect_estimate.value, pd.Series({"D": 0.0}), atol=1e-1)
+        effect_estimate = causal_test_case.estimate_effect(self.df)
+        pd.testing.assert_series_equal(effect_estimate.value, pd.Series({"D": 0.0}), atol=1e-1)
 
     def test_execute_test_observational_linear_regression_estimator_risk_ratio(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
         regression estimator."""
-        estimation_model = LinearRegressionEstimator(
-            base_test_case=self.base_test_case_D_A,
-            treatment_value=self.treatment_value,
-            control_value=self.control_value,
-            adjustment_set=self.minimal_adjustment_set,
-        )
         causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case_A_C,
             expected_causal_effect=self.expected_causal_effect,
-            estimator=estimation_model,
-            estimate_type="risk_ratio",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="D",
+                outcome_variable="A",
+                treatment_value=self.treatment_value,
+                control_value=self.control_value,
+                adjustment_set=self.causal_dag.identification(treatment_variable="D", outcome_variable="A"),
+            ),
+            effect_measure="risk_ratio",
         )
-        causal_test_result = causal_test_case.estimate_effect(self.df)
-        pd.testing.assert_series_equal(causal_test_result.effect_estimate.value, pd.Series(0.0), atol=1)
+        effect_estimate = causal_test_case.estimate_effect(self.df)
+        pd.testing.assert_series_equal(effect_estimate.value, pd.Series(0.0), atol=1)
 
-    def test_invalid_estimate_type(self):
+    def test_invalid_effect_measure(self):
         """Check that executing the causal test case returns the correct results for dummy data using a linear
         regression estimator."""
-        estimation_model = LinearRegressionEstimator(
-            base_test_case=self.base_test_case_D_A,
-            treatment_value=self.treatment_value,
-            control_value=self.control_value,
-            adjustment_set=self.minimal_adjustment_set,
-        )
         causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case_A_C,
             expected_causal_effect=self.expected_causal_effect,
-            estimator=estimation_model,
-            estimate_type="invalid",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="D",
+                outcome_variable="A",
+                treatment_value=self.treatment_value,
+                control_value=self.control_value,
+                adjustment_set=self.causal_dag.identification(treatment_variable="D", outcome_variable="A"),
+            ),
+            effect_measure="invalid",
         )
         with self.assertRaises(AttributeError):
             causal_test_case.execute_test(self.df)
@@ -211,42 +134,85 @@ def test_invalid_estimate_type(self):
     def test_execute_test_observational_linear_regression_estimator_squared_term(self):
         """Check that executing the causal test case returns the correct results for dummy data with a squared term
         using a linear regression estimator. C ~ 4*(A+2) + D + D^2"""
-        estimation_model = LinearRegressionEstimator(
-            base_test_case=self.base_test_case_A_C,
-            treatment_value=self.treatment_value,
-            control_value=self.control_value,
-            adjustment_set=self.minimal_adjustment_set,
-            formula=f"C ~ A + {'+'.join(self.minimal_adjustment_set)} + (D ** 2)",
-        )
         causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case_A_C,
             expected_causal_effect=self.expected_causal_effect,
-            estimator=estimation_model,
+            estimator=LinearRegressionEstimator(
+                treatment_variable="A",
+                outcome_variable="C",
+                treatment_value=self.treatment_value,
+                control_value=self.control_value,
+                adjustment_set=self.minimal_adjustment_set,
+                formula=f"C ~ A + {'+'.join(self.minimal_adjustment_set)} + (D ** 2)",
+            ),
+            effect_measure="ate",
         )
-        causal_test_result = causal_test_case.estimate_effect(self.df)
-        pd.testing.assert_series_equal(causal_test_result.effect_estimate.value, pd.Series(4.0), atol=1)
+        effect_estimate = causal_test_case.estimate_effect(self.df)
+        pd.testing.assert_series_equal(effect_estimate.value, pd.Series(4.0), atol=1)
 
     def test_estimate_params_with_formula(self):
         """Ensure estimate params is handled correctly when a formula is passed into the estimator object"""
 
-        estimator = LinearRegressionEstimator(
-            base_test_case=self.base_test_case_A_C,
-            adjustment_set=set(),
-            control_value=0,
-            treatment_value=1,
-            formula="C ~ A + D",
-            adjustment_config={"D": 1},
-        )
         causal_test_case = CausalTestCase(
-            base_test_case=self.base_test_case_A_C,
             expected_causal_effect=self.expected_causal_effect,
-            estimate_type="risk_ratio",
-            estimator=estimator,
+            effect_measure="risk_ratio",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="A",
+                outcome_variable="C",
+                control_value=0,
+                treatment_value=1,
+                formula="C ~ A + D",
+                adjustment_config={"D": 1},
+            ),
         )
         self.assertEqual(
             round(
-                causal_test_case.estimate_effect(self.df).effect_estimate.value[0],
+                causal_test_case.estimate_effect(self.df).value[0],
                 3,
             ),
             1.444,
         )
+
+    def test_to_dict(self):
+        causal_test_case = CausalTestCase(
+            name="A |- C",
+            expected_causal_effect=ExactValue(4),
+            effect_measure="coefficient",
+            estimator=LinearRegressionEstimator(
+                treatment_variable="A",
+                outcome_variable="C",
+                formula="C ~ A + D",
+            ),
+        )
+        causal_test_case.execute_test(self.df, adequacy=True)
+        test_case_dict = causal_test_case.to_dict()
+
+        expected = {
+            "name": "A |- C",
+            "skip": False,
+            "effect_measure": "coefficient",
+            "query": None,
+            "expected_effect": {"name": "ExactValue", "effect_type": "direct", "value": 4, "atol": 0},
+            "estimator": {
+                "name": "LinearRegressionEstimator",
+                "treatment_variable": "A",
+                "outcome_variable": "C",
+                "alpha": 0.05,
+                "adjustment_set": ["D"],
+                "formula": "C ~ A + D",
+            },
+            "result": {
+                "outcome": "PASS",
+                "passed": True,
+                "effect_measure": "coefficient",
+                "effect_estimate": {"A": 4.0},
+                "ci_low": {"A": 4.0},
+                "ci_high": {"A": 4.0},
+                "adequacy": {"kurtosis": {"A": 0.0}, "passing": 100, "successful": 100, "bootstrap_size": 100},
+            },
+        }
+
+        # Use json_normalize to avoid rounding errors
+        pd.testing.assert_frame_equal(
+            pd.json_normalize(expected).round(2),
+            pd.json_normalize(test_case_dict).round(2),
+        )
diff --git a/tests/testing_tests/test_metamorphic_relations.py b/tests/testing_tests/test_metamorphic_relations.py
deleted file mode 100644
index 4df2bc5c..00000000
--- a/tests/testing_tests/test_metamorphic_relations.py
+++ /dev/null
@@ -1,331 +0,0 @@
-import unittest
-import os
-import shutil, tempfile
-import json
-
-from causal_testing.specification.causal_dag import CausalDAG
-from causal_testing.specification.scenario import Scenario
-from causal_testing.testing.metamorphic_relation import (
-    ShouldCause,
-    ShouldNotCause,
-    generate_metamorphic_relations,
-    generate_metamorphic_relation,
-    generate_causal_tests,
-)
-from causal_testing.specification.variable import Input, Output
-from causal_testing.testing.base_test_case import BaseTestCase
-
-
-class TestMetamorphicRelation(unittest.TestCase):
-    def setUp(self) -> None:
-        self.temp_dir_path = tempfile.mkdtemp()
-        self.dag_dot_path = os.path.join(self.temp_dir_path, "dag.dot")
-        dag_dot = """digraph DAG { rankdir=LR; X1 -> Z; Z -> M; M -> Y; X2 -> Z; X3 -> M;}"""
-        with open(self.dag_dot_path, "w") as f:
-            f.write(dag_dot)
-        self.dcg_dot_path = os.path.join(self.temp_dir_path, "dcg.dot")
-        dcg_dot = """digraph dct { a -> b -> c -> d; d -> c; }"""
-        with open(self.dcg_dot_path, "w") as f:
-            f.write(dcg_dot)
-
-        X1 = Input("X1", float)
-        X2 = Input("X2", float)
-        X3 = Input("X3", float)
-        Z = Output("Z", float)
-        M = Output("M", float)
-        Y = Output("Y", float)
-        self.scenario = Scenario(variables={X1, X2, X3, Z, M, Y})
-        self.default_control_input_config = {"X1": 1, "X2": 2, "X3": 3}
-        self.default_treatment_input_config = {"X1": 2, "X2": 3, "X3": 3}
-
-    def tearDown(self) -> None:
-        shutil.rmtree(self.temp_dir_path)
-
-    def test_json_stub_invalid_estimator(self):
-        """Test if the ShouldCause MR passes all metamorphic tests where the DAG perfectly represents the program
-        and there is only a single input."""
-        causal_dag = CausalDAG(self.dag_dot_path)
-        causal_dag.remove_nodes_from(["X2", "X3"])
-        adj_set = list(causal_dag.direct_effect_adjustment_sets(["X1"], ["Z"])[0])
-        should_not_cause_mr = ShouldNotCause(BaseTestCase("X1", "Z"), adj_set)
-        with self.assertRaises(ValueError) as e:
-            should_not_cause_mr.to_json_stub(estimator="InvalidEstimator")
-            self.assertTrue(e.exception.startswith("Unsupported estimator estimator InvalidEstimator."))
-
-    def test_should_not_cause_json_stub(self):
-        """Test if the ShouldCause MR passes all metamorphic tests where the DAG perfectly represents the program
-        and there is only a single input."""
-        causal_dag = CausalDAG(self.dag_dot_path)
-        causal_dag.remove_nodes_from(["X2", "X3"])
-        adj_set = list(causal_dag.direct_effect_adjustment_sets(["X1"], ["Z"])[0])
-        should_not_cause_mr = ShouldNotCause(BaseTestCase("X1", "Z"), adj_set)
-        self.assertEqual(
-            should_not_cause_mr.to_json_stub(),
-            {
-                "effect": "direct",
-                "estimate_type": "coefficient",
-                "estimator": "LinearRegressionEstimator",
-                "expected_effect": {"Z": "NoEffect"},
-                "treatment_variable": "X1",
-                "name": "X1 _||_ Z",
-                "estimator_kwargs": {"formula": "Z ~ X1"},
-                "alpha": 0.05,
-                "skip": False,
-            },
-        )
-
-    def test_should_not_cause_logistic_json_stub(self):
-        """Test if the ShouldCause MR passes all metamorphic tests where the DAG perfectly represents the program
-        and there is only a single input."""
-        causal_dag = CausalDAG(self.dag_dot_path)
-        causal_dag.remove_nodes_from(["X2", "X3"])
-        adj_set = list(causal_dag.direct_effect_adjustment_sets(["X1"], ["Z"])[0])
-        should_not_cause_mr = ShouldNotCause(BaseTestCase("X1", "Z"), adj_set)
-        self.assertEqual(
-            should_not_cause_mr.to_json_stub(
-                effect_type="total", estimate_type="unit_odds_ratio", estimator="LogisticRegressionEstimator"
-            ),
-            {
-                "effect": "total",
-                "estimate_type": "unit_odds_ratio",
-                "estimator": "LogisticRegressionEstimator",
-                "expected_effect": {"Z": "NoEffect"},
-                "treatment_variable": "X1",
-                "name": "X1 _||_ Z",
-                "estimator_kwargs": {"formula": "Z ~ X1"},
-                "alpha": 0.05,
-                "skip": False,
-            },
-        )
-
-    def test_should_cause_json_stub(self):
-        """Test if the ShouldCause MR passes all metamorphic tests where the DAG perfectly represents the program
-        and there is only a single input."""
-        causal_dag = CausalDAG(self.dag_dot_path)
-        causal_dag.remove_nodes_from(["X2", "X3"])
-        adj_set = list(causal_dag.direct_effect_adjustment_sets(["X1"], ["Z"])[0])
-        should_cause_mr = ShouldCause(BaseTestCase("X1", "Z"), adj_set)
-        self.assertEqual(
-            should_cause_mr.to_json_stub(),
-            {
-                "effect": "direct",
-                "estimate_type": "coefficient",
-                "estimator": "LinearRegressionEstimator",
-                "expected_effect": {"Z": "SomeEffect"},
-                "estimator_kwargs": {"formula": "Z ~ X1"},
-                "treatment_variable": "X1",
-                "name": "X1 --> Z",
-                "alpha": 0.05,
-                "skip": False,
-            },
-        )
-
-    def test_should_cause_logistic_json_stub(self):
-        """Test if the ShouldCause MR passes all metamorphic tests where the DAG perfectly represents the program
-        and there is only a single input."""
-        causal_dag = CausalDAG(self.dag_dot_path)
-        causal_dag.remove_nodes_from(["X2", "X3"])
-        adj_set = list(causal_dag.direct_effect_adjustment_sets(["X1"], ["Z"])[0])
-        should_cause_mr = ShouldCause(BaseTestCase("X1", "Z"), adj_set)
-        self.assertEqual(
-            should_cause_mr.to_json_stub(
-                effect_type="total",
-                estimate_type="unit_odds_ratio",
-                estimator="LogisticRegressionEstimator",
-                skip=False,
-            ),
-            {
-                "effect": "total",
-                "estimate_type": "unit_odds_ratio",
-                "estimator": "LogisticRegressionEstimator",
-                "expected_effect": {"Z": "SomeEffect"},
-                "estimator_kwargs": {"formula": "Z ~ X1"},
-                "treatment_variable": "X1",
-                "name": "X1 --> Z",
-                "alpha": 0.05,
-                "skip": False,
-            },
-        )
-
-    def test_all_metamorphic_relations_implied_by_dag(self):
-        dag = CausalDAG(self.dag_dot_path)
-        print(dag)
-        dag.add_edge("Z", "Y")  # Add a direct path from Z to Y so M becomes a mediator
-        metamorphic_relations = generate_metamorphic_relations(dag)
-
-        expected_relations = [
-            ShouldCause(BaseTestCase("X1", "Z"), []),
-            ShouldNotCause(BaseTestCase("X1", "M"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X1", "Y"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X1", "X2"), []),
-            ShouldNotCause(BaseTestCase("X2", "X1"), []),
-            ShouldNotCause(BaseTestCase("X1", "X3"), []),
-            ShouldNotCause(BaseTestCase("X3", "X1"), []),
-            ShouldCause(BaseTestCase("Z", "M"), []),
-            ShouldCause(BaseTestCase("Z", "Y"), ["M"]),
-            ShouldCause(BaseTestCase("X2", "Z"), []),
-            ShouldNotCause(BaseTestCase("Z", "X3"), []),
-            ShouldNotCause(BaseTestCase("X3", "Z"), []),
-            ShouldCause(BaseTestCase("M", "Y"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X2", "M"), ["Z"]),
-            ShouldCause(BaseTestCase("X3", "M"), []),
-            ShouldNotCause(BaseTestCase("X2", "Y"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X3", "Y"), ["M", "Z"]),
-            ShouldNotCause(BaseTestCase("X2", "X3"), []),
-            ShouldNotCause(BaseTestCase("X3", "X2"), []),
-        ]
-
-        self.assertEqual(expected_relations, metamorphic_relations)
-
-    def test_all_metamorphic_relations_implied_by_dag_parallel(self):
-        dag = CausalDAG(self.dag_dot_path)
-        dag.add_edge("Z", "Y")  # Add a direct path from Z to Y so M becomes a mediator
-        metamorphic_relations = generate_metamorphic_relations(dag, threads=2)
-
-        expected_relations = [
-            ShouldCause(BaseTestCase("X1", "Z"), []),
-            ShouldNotCause(BaseTestCase("X1", "M"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X1", "Y"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X1", "X2"), []),
-            ShouldNotCause(BaseTestCase("X2", "X1"), []),
-            ShouldNotCause(BaseTestCase("X1", "X3"), []),
-            ShouldNotCause(BaseTestCase("X3", "X1"), []),
-            ShouldCause(BaseTestCase("Z", "M"), []),
-            ShouldCause(BaseTestCase("Z", "Y"), ["M"]),
-            ShouldCause(BaseTestCase("X2", "Z"), []),
-            ShouldNotCause(BaseTestCase("Z", "X3"), []),
-            ShouldNotCause(BaseTestCase("X3", "Z"), []),
-            ShouldCause(BaseTestCase("M", "Y"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X2", "M"), ["Z"]),
-            ShouldCause(BaseTestCase("X3", "M"), []),
-            ShouldNotCause(BaseTestCase("X2", "Y"), ["Z"]),
-            ShouldNotCause(BaseTestCase("X3", "Y"), ["M", "Z"]),
-            ShouldNotCause(BaseTestCase("X2", "X3"), []),
-            ShouldNotCause(BaseTestCase("X3", "X2"), []),
-        ]
-
-        self.assertEqual(expected_relations, metamorphic_relations)
-
-    def test_all_metamorphic_relations_implied_by_dag_ignore_cycles(self):
-        dcg = CausalDAG(self.dcg_dot_path, ignore_cycles=True)
-        metamorphic_relations = generate_metamorphic_relations(dcg, threads=2, nodes_to_ignore=set(dcg.cycle_nodes()))
-        should_cause_relations = [mr for mr in metamorphic_relations if isinstance(mr, ShouldCause)]
-        should_not_cause_relations = [mr for mr in metamorphic_relations if isinstance(mr, ShouldNotCause)]
-
-        # Check all ShouldCause relations are present and no extra
-
-        self.assertEqual(
-            should_cause_relations,
-            [
-                ShouldCause(BaseTestCase("a", "b"), []),
-            ],
-        )
-        self.assertEqual(
-            should_not_cause_relations,
-            [],
-        )
-
-    def test_generate_metamorphic_relation_(self):
-        dag = CausalDAG(self.dag_dot_path)
-        [metamorphic_relation] = generate_metamorphic_relation(("X1", "Z"), dag)
-        self.assertEqual(
-            metamorphic_relation,
-            ShouldCause(BaseTestCase("X1", "Z"), []),
-        )
-
-    def test_generate_causal_tests_ignore_cycles(self):
-        dcg = CausalDAG(self.dcg_dot_path, ignore_cycles=True)
-        relations = generate_metamorphic_relations(dcg, nodes_to_ignore=set(dcg.cycle_nodes()))
-        with tempfile.TemporaryDirectory() as tmp:
-            tests_file = os.path.join(tmp, "causal_tests.json")
-            generate_causal_tests(self.dcg_dot_path, tests_file, ignore_cycles=True)
-            with open(tests_file, encoding="utf8") as f:
-                tests = json.load(f)
-            expected = list(
-                map(
-                    lambda x: x.to_json_stub(skip=False),
-                    filter(
-                        lambda relation: len(list(dcg.predecessors(relation.base_test_case.outcome_variable))) > 0,
-                        relations,
-                    ),
-                )
-            )
-            self.assertEqual(tests["tests"], expected)
-
-    def test_generate_causal_tests(self):
-        dag = CausalDAG(self.dag_dot_path)
-        relations = generate_metamorphic_relations(dag)
-        with tempfile.TemporaryDirectory() as tmp:
-            tests_file = os.path.join(tmp, "causal_tests.json")
-            generate_causal_tests(self.dag_dot_path, tests_file)
-            with open(tests_file, encoding="utf8") as f:
-                tests = json.load(f)
-            expected = list(
-                map(
-                    lambda x: x.to_json_stub(skip=False),
-                    filter(
-                        lambda relation: len(list(dag.predecessors(relation.base_test_case.outcome_variable))) > 0,
-                        relations,
-                    ),
-                )
-            )
-            self.assertEqual(tests["tests"], expected)
-
-    def test_generate_causal_tests_test_inputs(self):
-        dag = CausalDAG(self.dag_dot_path)
-        relations = generate_metamorphic_relations(dag)
-        with tempfile.TemporaryDirectory() as tmp:
-            tests_file = os.path.join(tmp, "causal_tests.json")
-            generate_causal_tests(self.dag_dot_path, tests_file, test_inputs=True)
-            with open(tests_file, encoding="utf8") as f:
-                tests = json.load(f)
-            expected = list(
-                map(
-                    lambda x: x.to_json_stub(skip=False),
-                    relations,
-                )
-            )
-            self.assertEqual(tests["tests"], expected)
-
-    def test_shoud_cause_string(self):
-        sc_mr = ShouldCause(BaseTestCase("X", "Y"), ["A", "B", "C"])
-        self.assertEqual(str(sc_mr), "X --> Y | ['A', 'B', 'C']")
-
-    def test_shoud_not_cause_string(self):
-        sc_mr = ShouldNotCause(BaseTestCase("X", "Y"), ["A", "B", "C"])
-        self.assertEqual(str(sc_mr), "X _||_ Y | ['A', 'B', 'C']")
-
-    def test_equivalent_metamorphic_relations(self):
-        sc_mr_a = ShouldCause(BaseTestCase("X", "Y"), ["A", "B", "C"])
-        sc_mr_b = ShouldCause(BaseTestCase("X", "Y"), ["A", "B", "C"])
-        self.assertEqual(sc_mr_a == sc_mr_b, True)
-
-    def test_equivalent_metamorphic_relations_empty_adjustment_set(self):
-        sc_mr_a = ShouldCause(BaseTestCase("X", "Y"), [])
-        sc_mr_b = ShouldCause(BaseTestCase("X", "Y"), [])
-        self.assertEqual(sc_mr_a == sc_mr_b, True)
-
-    def test_equivalent_metamorphic_relations_different_order_adjustment_set(self):
-        sc_mr_a = ShouldCause(BaseTestCase("X", "Y"), ["A", "B", "C"])
-        sc_mr_b = ShouldCause(BaseTestCase("X", "Y"), ["C", "A", "B"])
-        self.assertEqual(sc_mr_a == sc_mr_b, True)
-
-    def test_different_metamorphic_relations_empty_adjustment_set_different_outcome(self):
-        sc_mr_a = ShouldCause(BaseTestCase("X", "Z"), [])
-        sc_mr_b = ShouldCause(BaseTestCase("X", "Y"), [])
-        self.assertEqual(sc_mr_a == sc_mr_b, False)
-
-    def test_different_metamorphic_relations_empty_adjustment_set_different_treatment(self):
-        sc_mr_a = ShouldCause(BaseTestCase("X", "Y"), [])
-        sc_mr_b = ShouldCause(BaseTestCase("Z", "Y"), [])
-        self.assertEqual(sc_mr_a == sc_mr_b, False)
-
-    def test_different_metamorphic_relations_empty_adjustment_set_adjustment_set(self):
-        sc_mr_a = ShouldCause(BaseTestCase("X", "Y"), ["A"])
-        sc_mr_b = ShouldCause(BaseTestCase("X", "Y"), [])
-        self.assertEqual(sc_mr_a == sc_mr_b, False)
-
-    def test_different_metamorphic_relations_different_type(self):
-        sc_mr_a = ShouldCause(BaseTestCase("X", "Y"), [])
-        sc_mr_b = ShouldNotCause(BaseTestCase("X", "Y"), [])
-        self.assertEqual(sc_mr_a == sc_mr_b, False)
diff --git a/tests/tutorial_tests/test_tutorials.py b/tests/tutorial_tests/test_tutorials.py
index e625910c..e2e9f272 100644
--- a/tests/tutorial_tests/test_tutorials.py
+++ b/tests/tutorial_tests/test_tutorials.py
@@ -2,9 +2,9 @@
 
 os.environ["JUPYTER_PLATFORM_DIRS"] = "1"
 
+import asyncio
 import sys
 import warnings
-import asyncio
 
 warnings.filterwarnings("ignore", category=DeprecationWarning, message=r"Jupyter is migrating.*")
 
@@ -12,8 +12,9 @@
     asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
 
 import pathlib
-import pytest
+
 import nbformat
+import pytest
 from nbclient.client import NotebookClient
 
 NOTEBOOK_DIR = pathlib.Path(__file__).parent.parent.parent / "docs" / "source" / "tutorials"