[1] Jason M. Altschuler, Jonathan Weed, and Philippe Rigollet. Near-linear time approximation algorithms for optimal transport via sinkhorn iteration. In NeurIPS, pages 1964-1974, 2017.
[2] Artem Betlei, Eustache Diemert, and Massih-Reza Amini. Uplift modeling with generalization guarantees. In SIGKDD, pages 55-65, 2021.
[3] Jose H. Blanchet, Arun Jambulapati, Carson Kent, and Aaron Sidford. Towards optimal running times for optimal transport. CoRR, abs/1810.07717, 2018.
[4] François Bolley, Arnaud Guillin, and Cedric Villani. Quantitative concentration inequalities for empirical measures on non-compact spaces. *Probability Theory and Related Fields*, 137(3): 541–593, 2007.
[5] Nicolas Bonneel, Michiel van de Panne, Sylvain Paris, and Wolfgang Heidrich. Displacement interpolation using lagrangian mass transport. ACM Trans. Graph., 30(6):158, 2011.
[6] M Alan Brookhart, Sebastian Schneeweiss, Kenneth J Rothman, Robert J Glynn, Jerry Avorn, and Til Stürmer. Variable selection for propensity score models. American journal of epidemiology, 163(12):1149-1156, 2006.
[7] Aaron Chadha and Yiannis Andreopoulos. Improved techniques for adversarial discriminative domain adaptation. IEEE Trans. Image Process., 29:2622-2637, 2020.
[8] Yale Chang and Jennifer G. Dy. Informative subspace learning for counterfactual inference. In AAAI, pages 1770-1776, 2017.
[9] Laetitia Chapel, Rémi Flamary, Haoran Wu, Cédric Févotte, and Gilles Gasso. Unbalanced optimal transport through non-negative penalized linear regression. In NeurIPS, pages 23270-23282, 2021.
[10] Arthur Charpentier, Emmanuel Flachaire, and Ewen Gallic. Optimal transport for counterfactual estimation: A method for causal inference. arXiv preprint arXiv:2301.07755, 2023.
[11] Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, and Keping Yang. Autodebias: Learning to debias for recommendation. In SIGIR, pages 21–30, 2021.
[12] Tian Qi Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. Neural ordinary differential equations. In NeurIPS, pages 6572-6583, 2018.
[13] Mingyuan Cheng, Xinru Liao, Quan Liu, Bin Ma, Jian Xu, and Bo Zheng. Learning disentangled representations for counterfactual regression via mutual information minimization. In SIGIR, pages 1802-1806, 2022.
[14] Lenaïc Chizat, Gabriel Peyré, Bernhard Schmitzer, and François-Xavier Vialard. Scaling algorithms for unbalanced optimal transport problems. Math. Comput., 87(314):2563-2609, 2018.
[15] José M Cordero, Víctor Cristóbal, and Daniel Santín. Causal inference on education policies: A survey of empirical studies using pisa, timss and pirls. J. Econ. Surv., 32(3):878-915, 2018.
[16] Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy. Joint distribution optimal transportation for domain adaptation. In NeurIPS, pages 3730-3739, 2017.
[17] Nicolas Courty, Rémi Flamary, Devis Tuia, and Alain Rakotomamonjy. Optimal transport for domain adaptation. IEEE Trans. Pattern Anal. Mach. Intell., 39(9):1853-1865, 2017.
[18] Richard K Crump, V Joseph Hotz, Guido W Imbens, and Oscar A Mitnik. Nonparametric tests for treatment effect heterogeneity. Rev. Econ. Stat., 90(3):389-405, 2008.
[19] Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. In NeurIPS, pages 2292-2300, 2013.