Causal-learn: Causal Discovery in Python
Yujia Zheng Biwei Huang Wei Chen Joseph Ramsey Mingming Gong Ruichu Cai Shohei Shimizu Peter Spirtes Kun Zhang
Causal discovery aims at revealing causal relations from observational data,
which is a fundamental task in science and engineering. We describe
$\textit{causal-learn}$, an open-source Python library for causal discovery.
This library focuses on bringing a comprehensive collection of causal discovery
methods to both practitioners and researchers. It provides easy-to-use APIs for
non-specialists, modular building blocks for developers, detailed documentation
for learners, and comprehensive methods for all. Different from previous
packages in R or Java, $\textit{causal-learn}$ is fully developed in Python,
which could be more in tune with the recent preference shift in programming
languages within related communities. The library is available at
https://github.com/py-why/causal-learn.