Python Package for Causal Discovery Based on LiNGAM
Abstract
Causal discovery is a methodology for learning causal graphs from data, and LiNGAM is a well-known model for causal discovery. This paper describes an open-source Python package for causal discovery based on LiNGAM. The package implements various LiNGAM methods under different settings like time series cases, multiple-group cases, mixed data cases, and hidden common cause cases, in addition to evaluation of statistical reliability and model assumptions. The source code is freely available under the MIT license at https://github.com/cdt15/lingam.
Cite
Text
Ikeuchi et al. "Python Package for Causal Discovery Based on LiNGAM." Machine Learning Open Source Software, 2023.Markdown
[Ikeuchi et al. "Python Package for Causal Discovery Based on LiNGAM." Machine Learning Open Source Software, 2023.](https://mlanthology.org/mloss/2023/ikeuchi2023jmlr-python/)BibTeX
@article{ikeuchi2023jmlr-python,
title = {{Python Package for Causal Discovery Based on LiNGAM}},
author = {Ikeuchi, Takashi and Ide, Mayumi and Zeng, Yan and Maeda, Takashi Nicholas and Shimizu, Shohei},
journal = {Machine Learning Open Source Software},
year = {2023},
pages = {1-8},
volume = {24},
url = {https://mlanthology.org/mloss/2023/ikeuchi2023jmlr-python/}
}