Causal Logistic Bandits with Counterfactual Fairness Constraints
Chen, Jiajun Tian, Jin Quinn, Christopher John
Chen, Jiajun
Tian, Jin
Quinn, Christopher John
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Department
Machine Learning
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Type
Conference proceeding
Date
2025
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Language
English
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Abstract
Artificial intelligence will play a significant role in decision making in numerous aspects of society. Numerous fairness criteria have been proposed in the machine learning community, but there remains limited investigation into fairness as defined through specified attributes in a sequential decision-making framework. In this paper, we focus on causal logistic bandit problems where the learner seeks to make fair decisions, under a notion of fairness that accounts for counterfactual reasoning. We propose and analyze an algorithm by leveraging primal-dual optimization for constrained causal logistic bandits where the non-linear constraints are a priori unknown and must be learned in time. We obtain sub-linear regret guarantees with leading term similar to that for unconstrained logistic bandits (Lee et al., 2024) while guaranteeing sub-linear constraint violations. We show how to achieve zero cumulative constraint violations with a small increase in the regret bound.
Citation
J. Chen, J. Tian, and C. J. Quinn, “Causal Logistic Bandits with Counterfactual Fairness Constraints,” Oct. 06, 2025, PMLR. [Online]. Available: https://proceedings.mlr.press/v267/chen25bk.html
Source
Proceedings of Machine Learning Research
Conference
42nd International Conference on Machine Learning, ICML 2025
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Source
42nd International Conference on Machine Learning, ICML 2025
Publisher
ML Research Press