causal-learn:基于Python的因果发现算法平台
来源:集智俱乐部 本文约1100字,建议阅读5分钟
本文为你介绍基于Python的统一算法基本框架。
Constrained-based causal discovery methods. Score-based causal discovery methods. Causal discovery methods based on constrained functional causal models. Hidden causal representation learning. Granger causal analysis. 多个独立的基础模块,比如独立性测试,评分函数,图操作,评测指标。 更多最新的因果发现算法,如gradient-based methods等。
GitHub: https://github.com/cmu-phil/causal-learn 文档: https://causal-learn.readthedocs.io/en/latest/ 简单使用案例:https://github.com/cmu-phil/causal-learn/tree/main/tests 建议反馈: 郑雨嘉:yujiazh@cmu.edu,陈薇:chenweiDelight@gmail.com
1. 基于Python的统一算法框架
2. 经典算法的官方实现
3. 持续更新,掌握领域前沿
1. 安装
pip install causal-learn
G = pc(data, alpha, indep_test, stable, uc_rule, uc_priority, mvpc, correction_name, background_knowledge)
3. 可视化与评测
G.to_nx_graph()G.draw_nx_graph(skel=False)
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