Software
Software and code package
NEW SCREEN Advanced System Solutions. Causalas. https://www.screen.co.jp/as/solution/causalas
D. Arpit, M. Fernandez, C. Liu, W. Yao, W. Yang, P. Josel, S. Heinecke, E. Hu, H. Wang, S. Hoi, C. Xiong, K. Zhang, J. C. Niebles. Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data. Arxiv preprint arXiv:2301.10859, 2023.
[pdf] [Google scholar]T. Ikeuchi, M. Ide, Y. Zeng, T. N. Maeda, and S. Shimizu. Python package for causal discovery based on LiNGAM. Journal of Machine Learning Research, 24(14):1−8, 2023.
[pdf] [github] [Google scholar]
[LiNGAM Python package: Tutorial slides]
[LiNGAM Pythonパッケージでできること: 紹介スライド]NEC. Causal analysis. https://jpn.nec.com/solution/causalanalysis/index.html
Neutral Co., Ltd. NTech Predict. https://ntech.inc/predict/
causal-learn: Causal Discovery for Python
[github]K. Zhang, S. Zhu, M. Kalander, I. Ng, J. Ye, Z. Chen, L. Pan. gCastle: A Python Toolbox for Causal Discovery. Arxiv preprint arXiv:2111.15155, 2021.
[pdf] [Google scholar]D. Kalainathan, O. Goudet, R. Dutta. Causal Discovery Toolbox: Uncovering causal relationships in Python. Journal of Machine Learning Research, 21(37): 1:5, 2020.
[pdf] [code] [Google scholar]M. Kalisch, M Mächler, Diego Colombo, Marloes H. Maathuis, Peter Bühlmann. Causal Inference Using Graphical Models with the R Package pcalg. Journal of Statistical Software, 47(11): 1-26, 2012.
[pdf] [code] [Google scholar]