Causal-learn: Causal Discovery in Python

TL;DR

Causal-learn is a comprehensive, user-friendly Python library that enables causal discovery from observational data, catering to both practitioners and researchers with diverse methods and easy APIs.

Contribution

It introduces a fully Python-based, modular library for causal discovery, filling a gap in accessible tools compared to existing R and Java packages.

Findings

Provides a wide range of causal discovery methods

Offers easy-to-use APIs for non-experts

Supports research and practical applications

Abstract

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.