时 间：2024年5月10日14:00-15:00
地 点：普陀校区理科大楼A1716
报告人：史成春 伦敦政治经济学院副教授
主持人：张思亮 华东师范大学助理教授
摘 要：
A/B testing is critical for modern technological companies to evaluate the effectiveness of newly developed products against standard baselines. This paper studies optimal designs that aim to maximize the amount of information obtained from online experiments to estimate treatment effects accurately. We propose three optimal allocation strategies in a dynamic setting where treatments are sequentially assigned over time. These strategies are designed to minimize the variance of the treatment effect estimator when data follow a non-Markov decision process or a (time-varying) Markov decision process. We further develop estimation procedures based on existing off-policy evaluation (OPE) methods and conduct extensive experiments in various environments to demonstrate the effectiveness of the proposed methodologies. In theory, we prove the optimality of the proposed treatment allocation design and establish upper bounds for the mean squared errors of the resulting treatment effect estimators.
报告人简介：
史成春博士，现任伦敦政治经济学院统计系副教授，曾在北卡罗来纳州立大学（North Carolina State University）获得统计学博士学位。他的研究主要集中在强化学习领域（Reinforcement Learning），特别是在策略评估（Policy Evaluation）、因果推断（Causal Inference）、半监督学习（Semi-Supervised Learning）等方面的应用与优化。史博士曾荣获Institute of Mathematical Statistics (IMS) Tweedie Award和Royal Statistical Society (RSS) Research Prize等奖项。