提出鲁棒的序列实验设计，提升A/B测试在模型不准时的可靠性。
Robust Sequential Experimental Design for A/B Testing
- 基于统一框架，兼容上下文探索与动态设置
- 理论证明可控制最坏情况下的估计误差
- 在真实和合成数据上验证了实际有效性
实验设计已成为提升A/B测试样本效率的强大方法，但现有方法严重依赖模型正确设定。本文研究模型误设下的鲁棒序列实验设计，提出涵盖上下文强化学习与动态场景的统一框架。理论上，证明该设计能控制处理效应估计的最坏情况均方误差。实证上，使用一家领先科技公司的真实数据集及合成数据验证了所提方法的有效性。
原文摘要 · Abstract (English)
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.
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