Bibliography

Ali, R. A.; Richardson, T. S. and Spirtes, P. (2009). Markov equivalence for ancestral graphs. Annals of Statistics 37, 2808–2837. ↩¹ ↩²

Brito, C. and Pearl, J. (2002). Generalized Instrumental Variables. In: Proceedings of UAI 2002, Vol. 18; pp. 85–93. ↩¹ ↩²

Chickering, D. M. (2002). Learning Equivalence Classes of Bayesian-Network Structures. Journal of Machine Learning Research 2, 445–498. ↩¹ ↩²

Dor, D. and Tarsi, M. (1992). A simple algorithm to construct a consistent extension of a partially oriented graph. Technical Report R-185 (Cognitive Systems Laboratory, UCLA). ↩¹ ↩²

Evans, R. J. (2016). Graphs for margins of Bayesian networks. Scandinavian Journal of Statistics 43, 625–648. ↩¹ ↩²

Hauser, A. and Bühlmann, P. (2012). Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. Journal of Machine Learning Research 13, 2409–2464. ↩¹

Henckel, L.; Perković, E. and Maathuis, M. H. (2022). Graphical Criteria for Efficient Total Effect Estimation Via Adjustment in Causal Linear Models. Journal of the Royal Statistical Society: Series B 84, 579–599. ↩¹ ↩² ↩³ ↩⁴ ↩⁵

Jaber, A.; Ribeiro, A.; Zhang, J. and Bareinboim, E. (2022). Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness. In: Advances in Neural Information Processing Systems, Vol. 35, edited by Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K. and Oh, A. (Curran Associates, Inc.); pp. 3679–3690. ↩¹ ↩² ↩³ ↩⁴ ↩⁵

Jeong, H.; Tian, J. and Bareinboim, E. (2022). Finding and Listing Front-door Adjustment Sets. In: Advances in Neural Information Processing Systems, Vol. 35, edited by Koyejo, S.; Mohamed, S.; Agarwal, A.; Belgrave, D.; Cho, K. and Oh, A. (Curran Associates, Inc.); pp. 33173–33185. ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

Kahn, A. B. (1962). Topological sorting of large networks. Communications of the ACM 5, 558–562. ↩¹

Kuipers, J. and Moffa, G. (2015). Uniform random generation of large acyclic digraphs. Statistics and Computing 25, 227–242. ↩¹ ↩² ↩³ ↩⁴

Lauritzen, S. L.; Dawid, A. P.; Larsen, B. N. and Leimer, H.-G. (1990). Independence properties of directed Markov fields. Networks 20, 491–505. ↩¹

Maathuis, M. H. and Colombo, D. (2015). A generalized back-door criterion. The Annals of Statistics 43, 1060–1088. ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰

Maathuis, M. H.; Kalisch, M. and Bühlmann, P. (2009). Estimating high-dimensional intervention effects from observational data. The Annals of Statistics 37, 3133–3164. ↩¹ ↩² ↩³ ↩⁴

Meek, C. (1995). Causal inference and causal explanation with background knowledge. In: Proceedings of UAI 1995; pp. 403–410. ↩¹ ↩² ↩³ ↩⁴ ↩⁵

Nandy, P.; Maathuis, M. H. and Richardson, T. S. (2017). Estimating the effect of joint interventions from observational data in sparse high-dimensional settings. The Annals of Statistics 45, 647–674. ↩¹ ↩²

Pearl, J. (2009). Causality: Models, Reasoning, and Inference. 2nd Edition (Cambridge University Press). ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

Pellet, J.-p. and Elisseeff, A. (2008). Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets. In: Advances in Neural Information Processing Systems, Vol. 21, edited by Koller, D.; Schuurmans, D.; Bengio, Y. and Bottou, L. (Curran Associates, Inc.). ↩¹ ↩²

Perković, E.; Kalisch, M. and Maathuis, M. H. (2017). Interpreting and using CPDAGs with background knowledge. In: Proceedings of UAI 2017. ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

Perković, E.; Textor, J.; Kalisch, M. and Maathuis, M. H. (2018). Complete Graphical Characterization and Construction of Adjustment Sets in Markov Equivalence Classes of Ancestral Graphs. Journal of Machine Learning Research 18, 1–62. ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷ ↩⁸ ↩⁹ ↩¹⁰ ↩¹¹ ↩¹² ↩¹³ ↩¹⁴

Peters, J. M.; Janzing, D. and Schölkopf, B. (2017). Elements of Causal Inference: Foundations and Learning Algorithms: Foundations and Learning Algorithms. *Adaptive Computation and Machine Learning series * (MIT Press, United States). ↩¹ ↩² ↩³

Richardson, T. and Spirtes, P. (2002). Ancestral graph Markov models. Annals of Statistics 30, 962–1030. ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶ ↩⁷

Richardson, T. S. (2003). Markov properties for acyclic directed mixed graphs. Scandinavian Journal of Statistics 30, 145–157. ↩¹

Shpitser, I. and Pearl, J. (2008). Complete Identification Methods for the Causal Hierarchy. Journal of Machine Learning Research 9, 1941–1979. ↩¹ ↩² ↩³ ↩⁴ ↩⁵

Tian, J. and Pearl, J. (2002). A General Identification Condition for Causal Effects. In: Proceedings of the 18th National Conference on Artificial Intelligence (AAAI 2002); pp. 567–573. ↩¹ ↩²

Verma, T. and Pearl, J. (1990). Equivalence and synthesis of causal models. In: Proceedings of UAI 1990. ↩¹

Wang, T.-Z.; Qin, T. and Zhou, Z.-H. (2023). Sound and complete causal identification with latent variables given local background knowledge. Artificial Intelligence 322, 103964. ↩¹ ↩² ↩³ ↩⁴ ↩⁵

Wang, T.-Z.; Tao, L.; Qin, T. and Zhou, Z.-H. (2025). Estimating possible causal effects with latent variables via adjustment and novel rule orientation. Artificial Intelligence 347, 104387. ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶

Wienöbst, M.; Bannach, M. and Liśkiewicz, M. (27–30 Jul 2021). Extendability of causal graphical models: Algorithms and computational complexity. In: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, Vol. 161 of Proceedings of Machine Learning Research, edited by de Campos, C. and Maathuis, M. H. (PMLR); pp. 1248–1257. ↩¹

Witte, J.; Henckel, L.; Maathuis, M. H. and Didelez, V. (2020). On efficient adjustment in causal graphs. Journal of Machine Learning Research 21, 1–45. ↩¹

van der Zander, B. and Liśkiewicz, M. (2020). Finding Minimal d-separators in Linear Time and Applications. In: Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2020), Vol. 115 of PMLR; pp. 637–647. ↩¹ ↩² ↩³ ↩⁴

van der Zander, B.; Textor, J. and Liśkiewicz, M. (2015). Efficiently Finding Conditional Instruments for Causal Inference. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI 2015); pp. 3243–3249. ↩¹ ↩²

Zhang, J. (2008). On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence 172, 1873–1896. ↩¹ ↩² ↩³ ↩⁴ ↩⁵ ↩⁶