Currently, we implemented causal effect estimation which is guaranteed to be unbiased (and even variance minimizing) for directed acyclic graphs (DAGs): regressing on all parents. However, this does not work if an adjacent edge is undirected. Therefore, we have to implement causal effect estimation using valid adjustment sets in completed partially directed acyclic graphs (CPDAGs) which are the output of the PC algorithm, see for example: https://www.jmlr.org/papers/v21/20-175.html.
Currently, we implemented causal effect estimation which is guaranteed to be unbiased (and even variance minimizing) for directed acyclic graphs (DAGs): regressing on all parents. However, this does not work if an adjacent edge is undirected. Therefore, we have to implement causal effect estimation using valid adjustment sets in completed partially directed acyclic graphs (CPDAGs) which are the output of the PC algorithm, see for example: https://www.jmlr.org/papers/v21/20-175.html.