Causal-learn: Causal Discovery in Python']Python

被引:0
|
作者
Zheng, Yujia [1 ]
Huang, Biwei [2 ]
Chen, Wei [3 ]
Ramsey, Joseph [1 ]
Gong, Mingming [4 ]
Cai, Ruichu [3 ]
Shimizu, Shohei [5 ,7 ]
Spirtes, Peter [1 ]
Zhang, Kun [1 ,6 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
[2] Univ Calif San Diego, San Diego, CA USA
[3] Guangdong Univ Technol, Guangzhou, Guangdong, Peoples R China
[4] Univ Melbourne, Melbourne, Australia
[5] Shiga Univ, Shiga, Japan
[6] Mohamed bin Zayed Univ Artificial Intelligence, Abu Dhabi, U Arab Emirates
[7] RIKEN, Wako, Japan
基金
美国国家卫生研究院; 国家重点研发计划;
关键词
Causal Discovery; !text type='Python']Python[!/text; Conditional Independence; Independence; Machine Learning; BAYESIAN NETWORKS; MODEL;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering. We describe 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 library is available at https://github.com/py-why/causal-learn.
引用
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页数:8
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