Bibliographic Details
| Title: |
Causal-learn: Causal Discovery in Python |
| Authors: |
Zheng, Yujia, Huang, Biwei, Chen, Wei, Ramsey, Joseph, Gong, Mingming, Cai, Ruichu, Shimizu, Shohei, Spirtes, Peter, Zhang, Kun |
| Source: |
Journal of Machine Learning Research 25 (2024) |
| Publication Year: |
2023 |
| Collection: |
Computer Science
Statistics |
| Subject Terms: |
Computer Science - Machine Learning, Statistics - Methodology, Statistics - Machine Learning |
| More Details: |
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. |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2307.16405 |
| Accession Number: |
edsarx.2307.16405 |
| Database: |
arXiv |
