# Group-Sensitive Offline Contextual Bandits

arXiv: https://arxiv.org/abs/2510.27123 · HTML: https://mlsanity.com/paper/2510.27123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Placed in

- Offline RL and imitation learning from data / [Offline RL algorithms](https://mlsanity.com/offlinerl/offline-rl-algorithms/)

## Builds on (most cited first, 9 of 9)

- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Achieving Fairness in the Stochastic Multi-armed Bandit Problem](https://mlsanity.com/paper/1907.10516/) (2019)
- [Variance Reduction for Faster Non-Convex Optimization](https://mlsanity.com/paper/1603.05643/) (2016)
- [The Price of Fair PCA: One Extra Dimension](https://mlsanity.com/paper/1811.00103/) (2018)
- [An Improved Convergence Analysis of Stochastic Variance-Reduced Policy Gradient](https://mlsanity.com/paper/1905.12615/) (2019)
- [Fairness of Exposure in Stochastic Bandits](https://mlsanity.com/paper/2103.02735/) (2021)
- [Pessimism for Offline Linear Contextual Bandits using ℓp Confidence Sets](https://mlsanity.com/paper/2205.10671/) (2022)
- [Achieving Counterfactual Fairness for Causal Bandit](https://mlsanity.com/paper/2109.10458/) (2021)
- [Distributionally Robust Policy Evaluation under General Covariate Shift in Contextual Bandits](https://mlsanity.com/paper/2401.11353/) (2024)

## Abstract (arXiv)

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.