References

Chevalier, Dominik, and Marie-Pier Côté. 2024. “From Point to Probabilistic Gradient Boosting for Claim Frequency and Severity Prediction.” arXiv Preprint arXiv:2412.14916. https://arxiv.org/abs/2412.14916.

Côté, Marie-Pier, Olivier Côté, and Arthur Charpentier. 2024. “Selection Bias in Insurance: Why Portfolio-Specific Fairness Fails to Extend Market-Wide.” Available at SSRN: 10.2139/Ssrn.5018749.

Fernandes Machado, Agathe, Suzie Grondin, Philipp Ratz, Arthur Charpentier, and François Hu. 2025. “EquiPy: Sequential Fairness Using Optimal Transport in Python.” arXiv Preprint arXiv:2503.09866. https://arxiv.org/abs/2503.09866.

Grubinger, Thomas, Achim Zeileis, and Karl-Peter Pfeiffer. 2014. “Evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in r.” Journal of Statistical Software 61 (1): 1–29. https://doi.org/10.18637/jss.v061.i01.

Hu, Francois, Philipp Ratz, and Arthur Charpentier. 2024. “A Sequentially Fair Mechanism for Multiple Sensitive Attributes.” Proceedings of the AAAI Conference on Artificial Intelligence 38 (11): 12502–10. https://doi.org/10.1609/aaai.v38i11.29143.

Ke, Guolin, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. 2017. “LightGBM: A Highly Efficient Gradient Boosting Decision Tree.” Advances in Neural Information Processing Systems 30: 3146–54.

Lin, Jimmy, Chudi Zhong, Diane Hu, Cynthia Rudin, and Margo Seltzer. 2022. “Generalized and Scalable Optimal Sparse Decision Trees.” arXiv Preprint arXiv:2006.08690. https://arxiv.org/abs/2006.08690.

Lindholm, Mathias, Ronald Richman, Andreas Tsanakas, and Mario V Wüthrich. 2022. “Discrimination-Free Insurance Pricing.” ASTIN Bulletin 52 (1): 55–89.

Lindholm, M., R. Richman, A. Tsanakas, and M. V. Wüthrich. 2024. “Sensitivity-Based Measures of Discrimination in Insurance Pricing.” Available at SSRN: Ssrn.com/Abstract=4897265.