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]