Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship

This repository contains the Python implementation for the paper "Causal Graph Learning via Distributional Invariance of Cause-Effect Relationship".

We have prepare a bash file run.sh that can be executed using bash run.sh.
This experiment is conducted on the Sachs dataset (realworld), link: https://www.science.org/doi/10.1126/science.1105809.

The generating code for data in the folder categorical can be found in the ./utils folder. The code for data in the folder notears can be fetched from the following github: https://github.com/xunzheng/notears [1]

Reference

[1] Zheng, X., Aragam, B., Ravikumar, P., & Xing, E. P. (2018). DAGs with NO TEARS: Continuous optimization for structure learning (NeurIPS 2018, Spotlight).