[18] N. Grgic-Hlaca, M. B. Zafar, K. P. Gummadi, and A. Weller. The case for process fairness in learning: Feature selection for fair decision making. In NIPS symposium on machine learning and the law, volume 1, page 11. Barcelona, Spain, 2016.
[19] X. Han, L. Zhang, Y. Wu, and S. Yuan. Achieving counterfactual fairness for anomaly detection. arXiv preprint arXiv:2303.02318, 2023.
[20] M. Hardt, E. Price, and N. Srebro. Equality of opportunity in supervised learning. Advances in neural information processing systems, 29:3315-3323, 2016.
[21] J. Joo and K. Kärkkäinen. Gender slopes: Counterfactual fairness for computer vision models by attribute manipulation. In Proceedings of the 2nd international workshop on fairness, accountability, transparency and ethics in multimedia, pages 1-5, 2020.
[22] M. M. Khalili, X. Zhang, and M. Abroshan. Fair sequential selection using supervised learning models. Advances in Neural Information Processing Systems, 34:28144-28155, 2021.
[23] M. M. Khalili, X. Zhang, and M. Abroshan. Loss balancing for fair supervised learning. In A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, editors, Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 16271-16290. PMLR, 23-29 Jul 2023.
[24] H. Kim, S. Shin, J. Jang, K. Song, W. Joo, W. Kang, and I.-C. Moon. Counterfactual fairness with disentangled causal effect variational autoencoder. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 8128-8136, 2021.
[25] R. Kohavi. Scaling up the accuracy of naive-bayes classifiers: A decision-tree hybrid. In Kdd, volume 96, pages 202-207, 1996.
[26] M. J. Kusner, J. Loftus, C. Russell, and R. Silva. Counterfactual fairness. Advances in neural information processing systems, 30, 2017.
[27] Y. Li, H. Chen, S. Xu, Y. Ge, and Y. Zhang. Personalized counterfactual fairness in recommendation. arXiv preprint arXiv:2105.09829, 2021.
[28] Y. Li, H. Chen, S. Xu, Y. Ge, and Y. Zhang. Towards personalized fairness based on causal notion. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1054–1063, 2021.
[29] P. Lohia. Counterfactual multi-token fairness in text classification. arXiv preprint arXiv:2202.03792, 2022.
[30] J. Ma, R. Guo, M. Wan, L. Yang, A. Zhang, and J. Li. Learning fair node representations with graph counterfactual fairness. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pages 695-703, 2022.
[31] J. Pearl et al. Models, reasoning and inference. Cambridge, UK: CambridgeUniversityPress, 19(2), 2000.
[32] F. Petersen, D. Mukherjee, Y. Sun, and M. Yurochkin. Post-processing for individual fairness. Advances in Neural Information Processing Systems, 34:25944-25955, 2021.
[33] S. R. Pfohl, T. Duan, D. Y. Ding, and N. H. Shah. Counterfactual reasoning for fair clinical risk prediction. In Machine Learning for Healthcare Conference, pages 325-358. PMLR, 2019.
[34] T.-H. Pham, X. Zhang, and P. Zhang. Fairness and accuracy under domain generalization. In The Eleventh International Conference on Learning Representations, 2023.
[35] Y. Qiang, C. Li, M. Brocanelli, and D. Zhu. Counterfactual interpolation augmentation (cia): A unified approach to enhance fairness and explainability of dnn. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI, pages 732–739, 2022.
[36] C. Russell, M. J. Kusner, J. Loftus, and R. Silva. When worlds collide: integrating different counterfactual assumptions in fairness. Advances in neural information processing systems, 30, 2017.