Group-Sensitive Offline Contextual Bandits

• URL: http://arxiv.org/abs/2510.27123v1
• Date: Fri, 31 Oct 2025 02:55:51 GMT
• Title: Group-Sensitive Offline Contextual Bandits
• Authors: Yihong Guo, Junjie Luo, Guodong Gao, Ritu Agarwal, Anqi Liu,
• Abstract summary: offline contextual bandits allow one to learn policies from historical/offline data without requiring online interaction.<n>Some groups might benefit more than others from the learned policy, raising concerns about fairness.<n>We study a group-sensitive fairness constraint in offline contextual bandits, reducing group-wise reward disparities.
• Score: 14.94229258597513
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline contextual bandits allow one to learn policies from historical/offline data without requiring online interaction. However, offline policy optimization that maximizes overall expected rewards can unintentionally amplify the reward disparities across groups. As a result, some groups might benefit more than others from the learned policy, raising concerns about fairness, especially when the resources are limited. In this paper, we study a group-sensitive fairness constraint in offline contextual bandits, reducing group-wise reward disparities that may arise during policy learning. We tackle the following common-parity requirements: the reward disparity is constrained within some user-defined threshold or the reward disparity should be minimized during policy optimization. We propose a constrained offline policy optimization framework by introducing group-wise reward disparity constraints into an off-policy gradient-based optimization procedure. To improve the estimation of the group-wise reward disparity during training, we employ a doubly robust estimator and further provide a convergence guarantee for policy optimization. Empirical results in synthetic and real-world datasets demonstrate that our method effectively reduces reward disparities while maintaining competitive overall performance.

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