[33] David Maxwell Chickering. Optimal structure identification with greedy search. Journal of Machine Learning Research, 3:507-554, 2002.
[34] Ali Rahimi and Benjamin Recht. Random features for large-scale kernel machines. In Proceedings of the Conference on Neural Information Processing Systems (NeurIPS), NIPS'07, page 1177-1184, Red Hook, NY, USA, 2007. Curran Associates Inc. ISBN 9781605603520.
[35] Peter Spirtes, Clark N Glymour, Richard Scheines, and David Heckerman. Causation, prediction, and search. MIT Press, 2000.
[36] Karren D. Yang, Abigail Katoff, and Caroline Uhler. Characterizing and learning equivalence classes of causal dags under interventions. In Proceedings of the International Conference on Machine Learning (ICML), 2018.
[37] Jonas Peters, Dominik Janzing, and Bernhard Schlkopf. Elements of Causal Inference: Foundations and Learning Algorithms. The MIT Press, 2017. ISBN 0262037319.
[38] Sarah Mameche, David Kaltenpoth, and Jilles Vreeken. Discovering invariant and changing mechanisms from data. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD '22, page 1242-1252, New York, NY, USA, 2022. Association for Computing Machinery. ISBN 9781450393850. doi: 10.1145/3534678.3539479. URL https://doi.org/10.1145/3534678.3539479.
[39] Christina Heinze-Deml, Jonas Peters, and Nicolai Meinshausen. Invariant causal prediction for nonlinear models. Journal of Causal Inference, 6(2), 2018.
[40] Dominik Rothenhäuser, Nicolai Meinshausen, Peter Buhlmann, and Jonas Peters. Anchor regression: Heterogeneous data meet causality. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 83(2):215-246, 2021.
[41] Daniel Eaton and Kevin Murphy. Exact bayesian structure learning from uncertain interventions. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), volume 2, pages 107-114, San Juan, Puerto Rico, 2007. PMLR.
[42] Alain Hauser and Peter Buhlmann. Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs. Journal of Machine Learning Research, 13(1):2409-2464, aug 2012. ISSN 1532-4435.
[43] Yuhao Wang, Liam Solus, Karren D. Yang, and Caroline Uhler. Permutation-based causal inference algorithms with interventions. In NIPS, 2017.
[44] Xiao-Hui Zhang, Louis Y Tee, Xiao-Gang Wang, Qun-Shan Huang, and Shi-Hua Yang. Off-target effects in crispr/cas9-mediated genome engineering. Molecular Therapy - Nucleic Acids, 4:e264, 2015. ISSN 2162-2531. doi: https://doi.org/10.1038/mtna.2015.37. URL https://www.sciencedirect.com/science/article/pii/S216225311630049X.
[45] Jonas Peters, Joris M. Mooij, Dominik Janzing, and Bernhard Scholkopf. Causal discovery with continuous additive noise models. J. Mach. Learn. Res., 15(1):2009-2053, jan 2014. ISSN 1532-4435.
[46] Alexander Marx and Jilles Vreeken. Telling cause from effect by local and global regression. Knowledge and Information Systems, 60(3):1277-1305, 2019.
[47] Alexander Marx and Jilles Vreeken. Formally justifying MDL-based inference of cause and effect, 2021.
[48] Alexander Hagele, Jonas Rothfuss, Lars Lorch, Vignesh Ram Somnath, Bernhard Scholkopf, and Andreas Krause. Bacadi: Bayesian causal discovery with unknown interventions. In Francisco Ruiz, Jennifer Dy, and Jan-Willem van de Meent, editors, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), volume 206 of Proceedings of Machine Learning Research, pages 1411-1436. PMLR, 25-27 Apr 2023. URL https://proceedings.mlr.press/v206/hagele23a.html.