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Causal-learn: Causal Discovery in Python

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arxiv 2307.16405 v1 pith:CBVFKWUQ submitted 2023-07-31 cs.LG stat.MEstat.ML

classification cs.LGstat.MEstat.ML
keywords causalcausal-learndiscoverylibrarypythoncomprehensivemethodstextit
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 26 citations worldwide. Full citation record

  1. Causal discovery with endogenous context variables

    cs.LG 2024-12 conditional novelty 6.0 of 10

    An adaptive constraint-based algorithm that provably recovers context-specific causal skeletons when the context variable is endogenous, under new sufficiency and acyclicity assumptions.

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