Robust sequential experimental design for A/B testing

Qianglin Wen, Xiangkun Wu, Chengchun Shi, Ting Li, Niansheng Tang, Yingying Zhang and Hongtu Zhu

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Experimental design has emerged as a powerful approach for improving the sample efficiency of A/B testing, yet existing designs rely critically on correctly specified models. We study robust sequential experimental design under model mis specification and develop a unified framework that covers both contextual bandit and dynamic settings. Theoretically, we prove that our de sign bounds the worst-case mean squared error of the estimated treatment effect. Empirically, we demonstrate the effectiveness of the proposed approach using synthetic and real-world datasets from a leading technology company.

JEL-codes: C1 (search for similar items in EconPapers)
Date: 2026-04-30
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Published in Proceedings of Machine Learning Research, 30, April, 2026. ISSN: 2640-3498

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