[25] S Chandra Mouli and Bruno Ribeiro. Asymmetry learning for counterfactually-invariant classification in OOD tasks. In International Conference on Learning Representations, 2022. URL https://openreview.net/forum?id=avgclFZ2211.
[26] Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur. Understanding the failure modes of out-of-distribution generalization. In International Conference on Learning Representations, 2021. URL https://openreview.net/forum?id=fSTD6NFIW_b.
[27] Judea Pearl. Causality. Cambridge University Press, 2 edition, 2009. doi:10.1017/CBO9780511803161.
[28] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543, 2014. URL http://www.aclweb.org/anthology/D14-1162.
[29] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. SQuAD: 100,000+ questions for machine comprehension of text. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 2383-2392, Austin, Texas, November 2016. Association for Computational Linguistics. doi:10.18653/v1/D16-1264. URL https://aclanthology.org/D16-1264.
[30] Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang. Distributionally robust neural networks for group shifts: On the importance of regularization for worst-case generalization. In International Conference on Learning Representations (ICLR), 2020.
[31] Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij. On causal and anticausal learning. In Proceedings of the 29th International Conference on International Conference on Machine Learning, ICML'12, page 459-466, Madison, WI, USA, 2012. Omnipress. ISBN 9781450312851.
[32] Indira Sen, Mattia Samory, Claudia Wagner, and Isabelle Augenstein. Counterfactually augmented data and unintended bias: The case of sexism and hate speech detection. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4716-4726, Seattle, United States, July 2022. Association for Computational Linguistics. doi:10.18653/v1/2022.naacl-main.347. URL https://aclanthology.org/2022.nacl-main.347.
[33] Claudia Shi, David Blei, and Victor Veitch. Adapting neural networks for the estimation of treatment effects. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://proceedings.neurips.cc/paper_files/paper/2019/file/8fb5f8be2aa9d6c64a04e3ab9f63feee-Paper.pdf.
[34] E. C. T. Inequalities. by g.h. hardy, j.e. littlewood and g. polya. 2nd edition. pp. xii, 324. 27s. 6d. 1952. (cambridge university press). The Mathematical Gazette, 37(321):236-236, 1953. doi:10.1017/S0025557200027455.
[35] Victor Veitch, Alexander D'Amour, Steve Yadlowsky, and Jacob Eisenstein. Counterfactual invariance to spurious correlations in text classification. In Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan, editors, Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 16196-16208, 2021. URL https://proceedings.neurips.cc/paper/2021/bit/8710ef761bbb29a6f9d12e4ef8e4379c-Abtract.html.
[36] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierrick Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Huggingface's transformers: State-of-the-art natural language processing, 2019. URL https://arxiv.org/abs/1910.03771.