[1] Elias Bareinboim and Judea Pearl. Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences, 113(27):7345-7352, 2016.
[2] Facundo Bromberg and Dimitris Margaritis. Improving the reliability of causal discovery from small data sets using argumentation. Journal of Machine Learning Research, 10(2), 2009.
[3] David Maxwell Chickering. Optimal structure identification with greedy search. Journal of machine learning research, 3(Nov):507-554, 2002.
[4] Max Chickering. Statistically efficient greedy equivalence search. In Conference on Uncertainty in Artificial Intelligence, pages 241-249. PMLR, 2020.
[5] Diego Colombo and Marloes H Maathuis. A modification of the pc algorithm yielding order-independent skeletons. CoRR, abs/1211.3295, 2012.
[6] Diego Colombo, Marloes H Maathuis, Markus Kalisch, and Thomas S Richardson. Learning high-dimensional directed acyclic graphs with latent and selection variables. The Annals of Statistics, pages 294-321, 2012.
[7] Zhongyi Hu and Robin Evans. Faster algorithms for markov equivalence. In Conference on Uncertainty in Artificial Intelligence, pages 739-748. PMLR, 2020.
[8] Steffen Lauritzen and Kayvan Sadeghi. Unifying markov properties for graphical models. The Annals of Statistics, 46(5):2251-2278, 2018.
[9] Steffen L Lauritzen. Graphical models, volume 17. Clarendon Press, 1996.
[10] Judea Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, 1988.
[11] Judea Pearl. Causal diagrams for empirical research. Biometrika, pages 669-688, 1995.
[12] Judea Pearl and James M Robins. Probabilistic evaluation of sequential plans from causal models with hidden variables. arXiv preprint arXiv:1302.4977, 2013.
[13] Judea Pearl and Nanny Wermuth. When can association graphs admit a causal interpretation. Selecting Models from Data: Artificial Intelligence and Statistics IV, 89:205-214, 1994.
[14] Joseph Ramsey, Peter Spirtes, and Jiji Zhang. Adjacency-faithfulness and conservative causal inference. In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, pages 401–408, 2006.
[15] Thomas Richardson and Peter Spirtes. Ancestral graph markov models. The Annals of Statistics, 30(4):962-1030, 2002.
[16] Rajen D. Shah and Jonas Peters. The hardness of conditional independence testing and the generalised covariance measure. Annals of Statistics, 48(3):1514-1538, 2020.
[17] Ilya Shpitser and Judea Pearl. Complete identification methods for the causal hierarchy. Journal of Machine Learning Research, 9:1941-1979, 2008.
[18] Peter Spirtes. An anytime algorithm for causal inference. In International Workshop on Artificial Intelligence and Statistics, pages 278-285. PMLR, 2001.
[19] Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. Causation, prediction, and search. MIT press, 2000.
[20] Johannes Textor, Alexander Idelberger, and Maciej Liskiewicz. Learning from pairwise marginal independencies. In Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, pages 882–891, 2015.
[21] Jin Tian and Judea Pearl. On the testable implications of causal models with hidden variables. In Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence, pages 519-527, 2002.