Causal-learn: Causal Discovery in Python
- URL: http://arxiv.org/abs/2307.16405v1
- Date: Mon, 31 Jul 2023 05:00:35 GMT
- Title: Causal-learn: Causal Discovery in Python
- Authors: Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong,
Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
- Abstract summary: Causal discovery aims at revealing causal relations from observational data.
$textitcausal-learn$ is an open-source Python library for causal discovery.
- Score: 53.17423883919072
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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