A Two-armed Bandit Framework for A/B Testing
Abstract
A/B testing is widely used in modern technology companies for policy evaluation and product deployment, with the goal of comparing the outcomes under a newly-developed policy against a standard control. Various causal inference and reinforcement learning methods developed in the literature are applicable to A/B testing. This paper introduces a two-armed bandit framework designed to improve the power of existing approaches. The proposed procedure consists of three main steps: (i) employing doubly robust estimation to generate pseudo-outcomes, (ii) utilizing a two-armed bandit framework to construct the test statistic, and (iii) applying a permutation-based method to compute the -value. We demonstrate the efficacy of the proposed method through asymptotic theories, numerical experiments and real-world data from a ridesharing company, showing its superior performance in comparison to existing methods.
Cite
@article{arxiv.2507.18118,
title = {A Two-armed Bandit Framework for A/B Testing},
author = {Jinjuan Wang and Qianglin Wen and Yu Zhang and Xiaodong Yan and Chengchun Shi},
journal= {arXiv preprint arXiv:2507.18118},
year = {2025}
}