Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
文献类型：期刊论文
| 作者 | Shi, Chengchun6; Wang, Xiaoyu5; Luo, Shikai4; Zhu, Hongtu3; Ye, Jieping2; Song, Rui1 |
| 刊名 | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000768738300001 |
| 出版者 | TAYLOR & FRANCIS INC |
| 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.North Carolina State Univ, Raleigh, NC USA 2.Univ Michigan, Ann Arbor, MI 48109 USA 3.Univ N Carolina, Chapel Hill, NC 27515 USA 4.ByteDance, Beijing, Peoples R China 5.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing, Peoples R China 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. |
入库方式： OAI收割
来源：数学与系统科学研究院
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