Causal-learn: Causal Discovery in Python
This library addresses the need for accessible causal discovery tools in Python, catering to a broad audience in science and engineering, but it is incremental as it ports existing methods to a new programming language.
The authors introduced causal-learn, an open-source Python library that provides a comprehensive collection of causal discovery methods, making these tools accessible to practitioners, researchers, and learners through easy-to-use APIs and modular components.
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.