Code for paper: Meek Separators and Their Applications in Targeted Causal Discovery (NeurIPS 2023).
arXiv link: https://arxiv.org/abs/2310.20075
For Algorithm 1 (Meek Separator), a function that takes a causal DAG as input and produces outputs as set I to find the Meek separator is in meekseparator.py.
The full pipeline to reproduce the comparisons in the paper can be found in subset_search/test_policy.ipynb.
In particular, the problem instances are generated using functions in subset_search/graphs. The algorithms, including Algorithm 2 (Atomic Adaptive Subset Search) and baselines, are implemented in subset_search/policys. The verification lower bound is computed by calling subset_search/subset_verify.py (original implementation from https://github.com/cxjdavin/subset-verification-and-search-algorithms-for-causal-DAGs).
The full pipeline to reproduce the comparisons in the paper can be found in causal_mean_matching/test_policy.ipynb (code adapted from https://github.com/uhlerlab/causal_mean_matching).
In particular, the problem instances are generated using functions in causal_mean_matching/graphs. The algorithms, including Algorithm 3 & 4 (Find Source and Causal Mean Matching) and baselines, are implemented in causal_mean_matching/policys.