SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph

This is the official code repository for SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph (AISTATS 2025) by Mátyás Schubert, Tom Claassen and Sara Magliacane.

SNAP.mp4

Please consult python3 main.py -h for parameters. A demo is provided in demo.ipynb. The SNAP algorithm is implemented in algorithms/snap.py.

Requirements

To reporoduce the results of the paper, all dependencies described below have to be installed. To use only SNAP, you can copy the self-contained code in algorithms/snap.py and install only the dependencies listed in there.

Python dependencies can be installed with pip3 install -r requirements.txt

R dependencies can be installed as follows

install.packages("BiocManager")
BiocManager::install(c("graph", "RBGL", "Rgraphviz"))
install.packages(c("pcalg", "igraph", "expm", "bnlearn", "dagitty"))

Install py-tetrad such that the repository is cloned into this directory, following the official github page.

The ldecc_github directory contains code adapted from https://github.com/acmi-lab/local-causal-discovery.

Citation

@inproceedings{schubert2025snap,
    title={{SNAP}: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph},
    author={M{\'a}ty{\'a}s Schubert and Tom Claassen and Sara Magliacane},
    booktitle={The 28th International Conference on Artificial Intelligence and Statistics},
    year={2025},
    url={https://openreview.net/forum?id=0gEjlLdjK9}
}