Abstract:  

This paper addresses a critical challenge in e-commerce experimentation: the bias in A/B testing on total demand caused by product stockouts. When products have limited inventory and may become unavailable during an experiment, standard randomized controlled trial (RCT) estimators systematically overestimate treatment effects. This occurs because stockouts create a dynamic interference between treatment and control groups, violating the Stable Unit Treatment Value Assumption. We develop a fluid limit analysis to characterize this bias and propose a novel Stockout-Discounted (SD) estimator that accounts for demand transfer patterns when products become unavailable. By applying appropriate discount weights based on product substitution behaviors, our estimator reduces bias while maintaining equal or lower variance compared to standard RCT approaches. We establish theoretical guarantees for our approach under general choice models without assuming specific underlying customer choice behavior. Our findings provide practical guidance for e-commerce platforms seeking accurate treatment effect estimation in inventory-constrained environments, where traditional A/B testing methods fail to account for the dynamic nature of product availability.

Bio:

Sentao Miao is an Assistant Professor of Operations Management in Leeds School of Business at University of Colorado Boulder. Previously, he was an Assistant Professor in Bensadoun School of Retail Management & Desautels Faculty of Management at McGill University. His research interests are mainly in developing efficient learning and optimization algorithms with various applications in Operations Management. For methodologies, Sentao Miao focuses on statistical and machine learning algorithms such as online learning, multi-arm bandit problem, reinforcement learning; he is also interested in approximation algorithms with provable performance. For applications, he mainly works on operations management problems such as dynamic pricing, assortment selection, inventory control, etc. Sentao Miao obtained his PhD degree in Department of Industrial and Operations Engineering at University of Michigan.

Organier: 周源