CAUSALDISCOVERY Procedure
References
Chen, X., Huang, T., and Chvosta, J. (2022). “Directed Acyclic Graph Machine Learning System.” US Patent 202211443198.
Chen, X., Kabisa, S. T., Frame, D., Chang, M.-C., Gu, W., Walton, G. E., and Elsheimer, D. B. (2024). “Techniques for Learning Causal Graphs.” US patent pending.
Chickering, D. M., Heckerman, D., and Meek, C. (2004). “Large-Sample Learning of Bayesian Networks Is NP-Hard.” Journal of Machine Learning Research 5:1287–1330.
Glymour, C., Zhang, K., and Spirtes, P. (2019). “Review of Causal Discovery Methods Based on Graphical Models.” Frontiers in Genetics 10:524. https://doi.org/10.3389/fgene.2019.00524.
Kitson, N. K., Constantinou, A. C., Guo, Z., Liu, Y., and Chobtham, K. (2021). “A Survey of Bayesian Network Structure Learning.” ArXiv:2109.11415.
Murphy, K. (2007). “How to Use the Bayes Net Toolbox.” Accessed September 29, 2021. http://bayesnet.github.io/bnt/docs/usage.html.
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco: Morgan Kaufmann.
Peters, J., Janzing, D., and Schölkopf, B. (2017). Elements of Causal Inference: Foundations and Learning Algorithms. Cambridge, MA: MIT Press.
Yu, Y., Gao, T., Yin, N., and Ji, Q. (2021). “DAGs with No Curl: An Efficient DAG Structure Learning Approach.” ML Research Press.