SlowGuess/NAACL_2022_OCR huggingface dataset:
Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C Lipton. 2021. Explaining the efficacy of counterfactually augmented data. In International Conference on Learning Representations.
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran Haque, Sara M Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang. 2021. WILDS: A benchmark of in-the-wild distribution shifts. In Proceedings of the 38th International Conference on Machine Learning, pages 5637-5664.
Kaiji Lu, Piotr Mardziel, Fangjing Wu, Preetam Amancharla, and Anupam Datta. 2020. Gender bias in neural natural language processing. In Logic, Language, and Security, pages 189-202. Springer.
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3428-3448.
Margaret Mitchell, Jacqui Aguilar, Theresa Wilson, and Benjamin Van Durme. 2013. Open domain targeted sentiment. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1643-1654.
Judea Pearl. 1995. Causal diagrams for empirical research. Biometrika, 82(4):669-688.
Judea Pearl. 2009. Causality. Cambridge university press.
Jonas Peters, Dominik Janzing, and Bernhard Scholkopf. 2017. Elements of causal inference: foundations and learning algorithms. The MIT Press.
Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018. Hypothesis only baselines in natural language inference. In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics, pages 180-191.
Marco Tulio Ribeiro, Tongshuang Wu, Carlos Guestrin, and Sameer Singh. 2020. Beyond accuracy: Behavioral testing of NLP models with CheckList. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4902-4912, Online. Association for Computational Linguistics.
Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris Mooij. 2012. On causal and anticausal learning. In Proceedings of the 29th International Coference on International Conference on Machine Learning, pages 459-466.
Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner. Anirudh Goyal, and Yoshua Bengio. 2021. Toward causal representation learning. Proceedings of the IEEE, 109(5):612-634.
Roy Schwartz and Gabriel Stanovsky. 2022. On the limitations of dataset balancing: The lost battle against spurious correlations. In *Findings of the Association for Computational Linguistics: NAACL* 2022.
Herbert A Simon. 1954. Spurious correlation: A causal interpretation. Journal of the American Statistical Association, 49(267):467-479.
Victor Veitch, Alexander D'Amour, Steve Yadlowsky, and Jacob Eisenstein. 2021. Counterfactual invariance to spurious correlations in text classification Advances in Neural Information Processing Systems, 34.