$\left[\mathrm{GKS}^{+}19\right]$  Kristjan Greenewald, Dmitriy Katz, Karthikeyan Shanmugam, Sara Magliacane, Murat Kocaoglu, Enric Boix-Adserà, and Guy Bresler. Sample Efficient Active Learning of Causal Trees. Advances in Neural Information Processing Systems, 32, 2019.
[GRE84] John R. Gilbert, Donald J. Rose, and Anders Edenbrandt. A Separator Theorem for Chordal Graphs. SIAM Journal on Algebraic Discrete Methods, 5(3):306-313, 1984.
[GSKB18] AmirEmad Ghassami, Saber Salehkaleybar, Negar Kiyavash, and Elias Bareinboim. Budgeted Experiment Design for Causal Structure Learning. In International Conference on Machine Learning, pages 1724-1733. PMLR, 2018.
[HB12] Alain Hauser and Peter Buhlmann. Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. The Journal of Machine Learning Research, 13(1):2409-2464, 2012.
[HB14] Alain Hauser and Peter Buhlmann. Two optimal strategies for active learning of causal models from interventional data. International Journal of Approximate Reasoning, 55(4):926-939, 2014.
[HDPM18] Christina Heinze-Deml, Jonas Peters, and Nicolai Meinshausen. Invariant causal prediction for nonlinear models. Journal of Causal Inference, 6(2), 2018.
[HSSC08] Aric Hagberg, Pieter Swart, and Daniel S Chult. Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008.
[KDV17] Murat Kocaoglu, Alex Dimakis, and Sriram Vishwanath. Cost-Optimal Learning of Causal Graphs. In International Conference on Machine Learning, pages 1875–1884. PMLR, 2017.
[Mee95] Christopher Meek. Causal Inference and Causal Explanation with Background Knowledge. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, UAI'95, page 403-410, San Francisco, CA, USA, 1995. Morgan Kaufmann Publishers Inc.
[PB14] Jonas Peters and Peter Buhlmann. Identifiability of Gaussian structural equation models with equal error variances. Biometrika, 101(1):219-228, 2014.
[Pea03] Judea Pearl. Causality: models, reasoning, and inference. Econometric Theory, 19(4):675-685, 2003.
[RHB00] James M Robins, Miguel Angel Hernan, and Babette Brumback. Marginal structural models and causal inference in epidemiology. Epidemiology, pages 550-560, 2000.
[SGSH00] Peter Spirtes, Clark N. Glymour, Richard Scheines, and David Heckerman. Causation, Prediction, and Search. MIT press, 2000.
[SKDV15] Karthikeyan Shanmugam, Murat Kocaoglu, Alexandros G. Dimakis, and Sriram Vishwanath. Learning Causal Graphs with Small Interventions. Advances in Neural Information Processing Systems, 28, 2015.
[SMG+20] Chandler Squires, Sara Magliacane, Kristjan Greenewald, Dmitriy Katz, Murat Kocaoglu, and Karthikeyan Shanmugam. Active Structure Learning of Causal DAGs via Directed Clique Trees. Advances in Neural Information Processing Systems, 33:21500-21511, 2020.
[SU22] Chandler Squires and Caroline Uhler. Causal structure learning: a combinatorial perspective. Foundations of Computational Mathematics, pages 1-35, 2022.
[VP90] Thomas Verma and Judea Pearl. Equivalence and Synthesis of Causal Models. In Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence, UAI '90, page 255-270, USA, 1990. Elsevier Science Inc.