OpenReview.net, 2018. URL https://openreview.net/forum?id=BJE-4xWOW. (page 1, 2)
[21] Matt J. Kusner, Joshua R. Loftus, Chris Russell, and Ricardo Silva. Counterfactual fairness. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 4066-4076, 2017. URL https://proceedings.neurips.cc/paper/2017/bit/ a486cd07e4ac3d270571622f4f316ec5-Abstraction.html. (page 9, 10)
[22] Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, and Matthew Le. Flow matching for generative modeling. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=PqvMRDCJT9t. (page 10)
[23] Arash Nasr-Esfahany, MohammadIman Alizadeh, and Devavrat Shah. Counterfactual identifiability of bijective causal models. ArXiv preprint, abs/2302.02228, 2023. URL https://arxiv.org/abs/2302.02228. (page 1, 10)
[24] George Papamakarios, Iain Murray, and Theo Pavlakou. Masked autoregressive flow for density estimation. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, pages 2338-2347, 2017. URL https://proceedings.neurips.cc/paper/2017/bit/6c1da886822c67822bcf3679d04369fa-AAbstract.html. (page 3, 8, 27, 28)
[25] George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. J. Mach. Learn. Res., 22:57:1-57:64, 2021. URL http://jmlr.org/papers/v22/19-1028.html. (page 3, 6, 16)
[26] Álvaro Parafita and Jordi Vitrià. Estimand-agnostic causal query estimation with deep causal graphs. IEEE Access, 10:71370–71386, 2022. doi: 10.1109/ACCESS.2022.3188395. (page 1, 2)
[27] Nick Pawlowski, Daniel Coelho de Castro, and Ben Glocker. Deep structural causal models for tractable counterfactual inference. In Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020. URL https://proceedings.neurips.cc/paper/2020/bit/0987b8b338d6c90bbeddd8631bc499221-AAbstract.html. (page 1, 2)
[28] J. Pearl. Causality. Cambridge University Press, 2009. ISBN 9781139643986. URL https://books.google.de/books?id=LLkhAwAAQBAJ. (page 1, 2, 7)
[29] Judea Pearl. Causal inference in statistics: An overview. 2009. (page 8)
[30] Judea Pearl. The do-calculus revisited. In Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, Catalina Island, CA, USA, August 14-18, 2012, pages 3-11. AUAI Press, 2012. URL https://dslpitt.org/uai/displayArticleDetails.jsp?mmnu=1&smnu=2&article_id=2330&proceeding_id=28. (page 6, 7, 21)
[31] Murray Rosenblatt. Remarks on a multivariate transformation. The Annals of Mathematical Statistics, 23(3):470-472, 1952. ISSN 00034851. URL http://www.jstor.org/stable/2236692. (page 4, 18)
[32] Pedro Sanchez and Sotirios A. Tsaftaris. Diffusion causal models for counterfactual estimation. In CLEaR, 2022. (page 2)
[33] Edward H Simpson. The interpretation of interaction in contingency tables. Journal of the Royal Statistical Society: Series B (Methodological), 13(2):238-241, 1951. (page 8)
[34] Pablo Sánchez-Martin, Miriam Rateike, and Isabel Valera. Vaca: Designing variational graph autoencoders for causal queries. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7):8159-8168, Jun. 2022. doi: 10.1609/aaai.v36i7.20789. URL https://ojs.aaaai.org/index.php/AAAI/article/view/20789. (page 2, 5, 6, 8, 9, 23, 27, 28, 29)
[35] Robert Tarjan. Depth-first search and linear graph algorithms. SIAM Journal on Computing, 1(2):146-160, 1972. doi: 10.1137/0201010. URL https://doi.org/10.1137/0201010. (page 4, 17)
[36] Julius von Kugelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Scholkopf, Michel Besserve, and Francesco Locatello. Self-supervised learning with data augmentations