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Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
Shi, Chengchun1; Wang, Xiaoyu2; Luo, Shikai3; Zhu, Hongtu4; Ye, Jieping5; Song, Rui6
2022-03-12
Source PublicationJOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN0162-1459
Pages13
AbstractA/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.
KeywordA/B testing Causal inference Online experiment Online updating Reinforcement learning Sequential testing
DOI10.1080/01621459.2022.2027776
Indexed BySCI
Language英语
Funding ProjectLSE's Research Support Fund in 2021 ; [NSF-DMS-1555244] ; [2113637]
WOS Research AreaMathematics
WOS SubjectStatistics & Probability
WOS IDWOS:000768738300001
PublisherTAYLOR & FRANCIS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/60194
Collection中国科学院数学与系统科学研究院
Corresponding AuthorShi, Chengchun
Affiliation1.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
Recommended Citation
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.
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