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.

翻译：因果发现旨在从观测数据中揭示因果关系，这是科学与工程领域的一项基础性任务。我们介绍了一个用于因果发现的开源Python库——causal-learn。该库致力于为实践者和研究者提供全面的因果发现方法集合。它为非专业人员提供了易于使用的API，为开发者提供了模块化构建块，为学习者提供了详细文档，并为所有用户提供了全面的方法。与以往基于R或Java的软件包不同，causal-learn完全基于Python开发，这更契合相关社区近期对编程语言偏好的转变。该库可通过https://github.com/py-why/causal-learn获取。