题　目：A Reinforcement Learning Framework for A/B Testing

嘉　宾：史成春，副教授，伦敦政治经济学院

主持人：孙海龙 助理教授 上海交通大学安泰经济与管理学院

时　间：2024年11月8日（周五）10:00-11:30am

地　点：安泰楼A511室

内容简介：

A/B testing, or online experiment is a standard business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries. Major challenges arise in online experiments of two-sided marketplace platforms (e.g., Uber) where there is only one unit that receives a sequence of treatments over time. In those experiments, the treatment at a given time impacts current outcome as well as future outcomes. The aim of this paper is to introduce a reinforcement learning framework for carrying A/B testing in these experiments, while characterizing the long-term treatment effects. Our proposed testing procedure allows for sequential monitoring and online updating. It is generally applicable to a variety of treatment designs in different industries. In addition, we systematically investigate the theoretical properties (e.g., size and power) of our testing procedure. Finally, we apply our framework to both simulated data and a real-world data example obtained from a technological company to illustrate its advantage over the current practice. A Python implementation of our test is available at https://github.com/callmespring/CausalRL.

演讲人简介：

Chengchun Shi is an Associate Professor at London School of Economics and Political Science. He is serving as the associate editors of top statistics journals JRSSB, JASA. His research focuses on reinforcement learning, with applications to healthcare, ridesharing, video-sharing and neuroimaging. He has published over 50 papers, in both highly-ranked machine learning conferences (e.g., NeurIPS, ICML, KDD, AISTATS) and statistics journals (e.g., JASA, JRSSB, AoS). He was the recipient of the Royal Statistical Society Research Prize in 2021 and IMS Tweedie Award in 2024.

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