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

doi:10.1080/01621459.2022.2027776

CSpace

	Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
	Shi, Chengchun 1; Wang, Xiaoyu 2; Luo, Shikai 3; Zhu, Hongtu 4; Ye, Jieping 5; Song, Rui 6
	2022-03-12
发表期刊	JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN	0162-1459
页码	13
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.
关键词	A/B testing Causal inference Online experiment Online updating Reinforcement learning Sequential testing
DOI	10.1080/01621459.2022.2027776
收录类别	SCI
语种	英语
资助项目	LSE's Research Support Fund in 2021 ; [NSF-DMS-1555244] ; [2113637]
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000768738300001
出版者	TAYLOR & FRANCIS INC
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/60194
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.London Sch Econ & Polit Sci, London, England 2.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing, Peoples R China 3.ByteDance, Beijing, Peoples R China 4.Univ N Carolina, Chapel Hill, NC 27515 USA 5.Univ Michigan, Ann Arbor, MI 48109 USA 6.North Carolina State Univ, Raleigh, NC USA
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.