Robust Sequential Experimental Design for A/B Testing
For practitioners running A/B tests, this provides a theoretically grounded method that remains reliable when models are misspecified.
The paper develops a robust sequential experimental design framework for A/B testing that handles model misspecification, proving worst-case MSE bounds and showing effectiveness on synthetic and real-world data from a tech company.
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.