[23] Ricky T.Q. Chen et al. “Neural ordinary differential equations”. In: Advances in Neural Information Processing Systems. 2018.
[24] Ricky T.Q. Chen et al. "Residual flows for invertible generative modeling". In: Advances in Neural Information Processing Systems. 2019.
[25] Scott Chen and Ramesh Gopinath. "Gaussianization". In: Advances in Neural Information Processing Systems. 2000.
[26] Florentin Coeurdoux, Nicolas Dobigeon, and Pierre Chainais. "Sliced-Wasserstein normalizing flows: Beyond maximum likelihood training". In: European Symposium on Artificial Neural Networks. 2022.
[27] Juan Correa, Sanghack Lee, and Elias Bareinboim. "Nested counterfactual identification from arbitrary surrogate experiments". In: Advances in Neural Information Processing Systems. 2021.
[28] Alicia Curth and Mihaela van der Schaar. "Nonparametric estimation of heterogeneous treatment effects: from theory to learning algorithms". In: International Conference on Artificial Intelligence and Statistics. 2021.
[29] Saloni Dash, Vineeth N. Balasubramanian, and Amit Sharma. "Evaluating and mitigating bias in image classifiers: A causal perspective using counterfactuals". In: IEEE/CVF Winter Conference on Applications of Computer Vision. 2022.
[30] A. Philip Dawid. "Influence diagrams for causal modelling and inference". In: International Statistical Review 70.2 (2002), pp. 161-189.
[31] Edward De Brouwer. "Deep counterfactual estimation with categorical background variables". In: Advances in Neural Information Processing Systems. 2022.
[32] Lucas De Lara et al. "Transport-based counterfactual models". In: arXiv preprint arXiv:2108.13025 (2021).
[33] Laurent Dinh, David Krueger, and Yoshua Bengio. "NICE: Non-linear independent components estimation". In: arXiv preprint arXiv:1410.8516 (2014).
[34] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. "Density estimation using real NVP". In: International Conference on Learning Representations. 2017.
[35] Jacob Dorn and Kevin Guo. "Sharp sensitivity analysis for inverse propensity weighting via quantile balancing". In: Journal of the American Statistical Association (2022), pp. 1-13.
[36] Tom Everitt et al. "Agent incentives: A causal perspective". In: AAAI Conference on Artificial Intelligence. 2021.
[37] Yanqin Fan and Sang Soo Park. "Sharp bounds on the distribution of treatment effects and their statistical inference". In: Econometric Theory 26.3 (2010), pp. 931-951.
[38] Jakob Foerster et al. "Counterfactual multi-agent policy gradients". In: AAAI Conference on Artificial Intelligence. 2018.
[39] Dennis Frauen and Stefan Feuerriegel. "Estimating individual treatment effects under unobserved confounding using binary instruments". In: International Conference on Learning Representations. 2023.
[40] Dennis Frauen, Valentyn Melnychuk, and Stefan Feuerriegel. "Sharp bounds for generalized causal sensitivity analysis". In: Advances in Neural Information Processing Systems. 2023.
[41] Dennis Frauen et al. “A neural framework for generalized causal sensitivity analysis”. In: arXiv preprint arXiv:2311.16026 (2023).
[42] Sainyam Galhotra, Romila Pradhan, and Babak Salimi. "Explaining black-box algorithms using probabilistic contrastive counterfactuals". In: International Conference on Management of Data. 2021.
[43] Ron Goldman. "Curvature formulas for implicit curves and surfaces". In: Computer Aided Geometric Design 22.7 (2005), pp. 632-658.
[44] Yash Goyal et al. "Counterfactual visual explanations". In: International Conference on Machine Learning. 2019.
[45] Will Grathwohl et al. "Scalable reversible generative models with free-form continuous dynamics". In: International Conference on Learning Representations. 2019.
[46] Luigi Gresele et al. "Causal inference through the structural causal marginal problem". In: International Conference on Machine Learning. 2022.