Python package for causal discovery based on LiNGAM
Takashi Ikeuchi, Mayumi Ide, Yan Zeng, Takashi Nicholas Maeda, Shohei Shimizu; 24(14):1−8, 2023.
Abstract
This article presents an open-source Python package that focuses on causal discovery using LiNGAM (Linear Non-Gaussian Acyclic Models). Causal discovery is a methodology for identifying causal relationships from data, and LiNGAM is a well-established model for this purpose. The package provides various LiNGAM methods tailored to different scenarios, including time series cases, multiple-group cases, mixed data cases, and hidden common cause cases. Additionally, the package allows for the evaluation of statistical reliability and model assumptions. The source code is freely available under the MIT license at https://github.com/cdt15/lingam.
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