Robust Sequential Experimental Design for A/B Testing
云南大学云南统计建模与数据分析重点实验室 · Zhejiang University · London School of Economics and Political Science · Shanghai University of Finance and Economics · Yunnan University · East China Normal University · University of North Carolina at Chapel Hill
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摘要
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 misspecification and develop a unified framework that covers both contextual bandit and dynamic settings. Theoretically, we prove that our design 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.