Yujia Zheng, a Ph.D. student at CMU, talks about the causal-learn package and how it can be used to learn causal graphs (and more) from observational data.

Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. This talk introduces 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, 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 talk will also explore related usage examples, aiming to further lower the entry threshold by providing a roadmap for selecting the appropriate algorithm.

**PyWhy Causality in Practice**: A talk series focusing on causality and machine learning, especially from a practical perspective. We'll have tutorials and presentations about PyWhy libraries but also talks by external speakers working on causal inference.
https://www.pywhy.org/community/videos