Software and code packages

LiNGAM and related causal discovery packages

• 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.
  paper GitHub

• Y. Zheng, B. Huang, W. Chen, J. Ramsey, M. Gong, R. Cai, S. Shimizu, P. Spirtes, and K. Zhang.
  Causal-learn: Causal discovery in Python.
  Journal of Machine Learning Research, 25(60): 1–8, 2024.
  A Python package for causal discovery methods including conditional-independence-based methods and LiNGAM methods.
  paper GitHub

• R. Lee, R. K. W. Wong, and Y. Ni.
  cyclinbayes: Bayesian Causal Discovery with Linear Non-Gaussian Directed Acyclic and Cyclic Graphical Models.
  arXiv:2602.21170, 2026.
  paper GitHub arXiv

• S. Nouri, K. Shao, and S. Safavi.
  TranCIT: Transient Causal Interaction Toolbox.
  arXiv:2509.00602, 2025.
  paper

• J. D. Ramsey and B. Andrews.
  Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search.
  arXiv:2308.07346, 2023.
  paper arXiv

• K. Zhang, S. Zhu, M. Kalander, I. Ng, J. Ye, Z. Chen, and L. Pan.
  gCastle: A Python Toolbox for Causal Discovery.
  arXiv:2111.15155, 2021.
  paper

• D. Kalainathan, O. Goudet, and R. Dutta.
  Causal Discovery Toolbox: Uncovering causal relationships in Python.
  Journal of Machine Learning Research, 21(37): 1–5, 2020.
  paper code

• M. Kalisch, M. Mächler, D. Colombo, M. H. Maathuis, and P. Bühlmann.
  Causal Inference Using Graphical Models with the R Package pcalg.
  Journal of Statistical Software, 47(11): 1–26, 2012.
  paper code

Industrial and applied tools

• SCREEN Advanced System Solutions.
  Causalas.
  Website

• NTT Communications Corporation.
  Node-AI.
  Website

• NEC.
  Causal analysis.
  Website

• Neutral Co., Ltd.
  NTech Predict.
  Website

Other platforms and benchmarking systems

• D. Arpit, M. Fernandez, C. Liu, W. Yao, W. Yang, P. Josel, S. Heinecke, E. Hu, H. Wang, S. Hoi, C. Xiong, K. Zhang, and J. C. Niebles.
  Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data.
  arXiv:2301.10859, 2023.
  paper

• S. J. Unni, P. Sheth, K. Ding, H. Liu, and K. S. Candan.
  UPREVE: An End-to-End Causal Discovery Benchmarking System.
  arXiv:2307.13757, 2023.
  paper

• J. Zhu, W. Chen, and R. Cai.
  CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference.
  arXiv:2602.11527, 2026.
  paper

• A. Kapkiç, P. Mandal, A. Gorantla, S. Wan, E. Çoban, P. Sheth, H. Liu, and K. S. Candan.
  CausalBench-ER: Causally-Informed Explanations and Recommendations for Reproducible Benchmarking.
  In Proc. CIKM 2025, pp. 6426–6431, 2025.
  paper

Reviews and guides

• F. L. Rios, G. Moffa, and J. Kuipers.
  Benchpress: A Versatile Platform for Structure Learning in Causal and Probabilistic Graphical Models.
  Journal of Statistical Software, 114(12), 2025.
  paper Journal of Statistical Software

• F. Canonaco, J. Gaudillo, N. Astrologo, F. Stella, and E. Acerbi.
  A guide to Bayesian networks software for structure and parameter learning, with a focus on causal discovery tools.
  Frontiers in Systems Biology, 5:1631901, 2025.
  paper

• I. Saleem and A. Irfan.
  Machine learning made easy: a beginner's guide for causal inference and discovery methods using Python.
  International Journal of Data Analysis Techniques and Strategies, 17(1): 36–53, 2025.
  paper