Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework

doi:10.1080/01621459.2022.2027776

	Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
	Shi, Chengchun 6; Wang, Xiaoyu 2; Luo, Shikai 3; Zhu, Hongtu 1; Ye, Jieping 4; Song, Rui 5
刊名	JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
	2022-03-12
页码	13
关键词	A/B testing Causal inference Online experiment Online updating Reinforcement learning Sequential testing
ISSN号	0162-1459
DOI	10.1080/01621459.2022.2027776
英文摘要	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 article 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 . for this article are available online.
资助项目	LSE's Research Support Fund in 2021 ; [NSF-DMS-1555244] ; [2113637]
WOS研究方向	Mathematics
语种	英语
出版者	TAYLOR & FRANCIS INC
WOS记录号	WOS:000768738300001
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/60193]
专题	中国科学院数学与系统科学研究院
通讯作者	Shi, Chengchun
作者单位	1.Univ N Carolina, Chapel Hill, NC 27515 USA 2.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing, Peoples R China 3.ByteDance, Beijing, Peoples R China 4.Univ Michigan, Ann Arbor, MI 48109 USA 5.North Carolina State Univ, Raleigh, NC USA 6.London Sch Econ & Polit Sci, London, England
推荐引用方式 GB/T 7714	Shi, Chengchun,Wang, Xiaoyu,Luo, Shikai,et al. Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework[J]. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,2022:13.
APA	Shi, Chengchun,Wang, Xiaoyu,Luo, Shikai,Zhu, Hongtu,Ye, Jieping,&Song, Rui.(2022).Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework.JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION,13.
MLA	Shi, Chengchun,et al."Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework".JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION (2022):13.