Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
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DOI：
10.1080/01621459.2022.2027776
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发表时间：
2020-02
影响因子：
3.7
通讯作者：
C. Shi;Xiaoyu Wang;S. Luo;Hongtu Zhu;Jieping Ye;R. Song
中科院分区：
文献类型：
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作者：
C. Shi;Xiaoyu Wang;S. Luo;Hongtu Zhu;Jieping Ye;R. Song
摘要A/B测试，或在线实验，是制药、技术和传统行业比较新产品和旧产品的标准商业策略。主要挑战出现在双边市场平台(例如优步)的在线试验中，其中只有一个单位随着时间的推移接受一系列治疗。在这些实验中，在给定时间的治疗会影响当前结果以及未来结果。本文的目的是介绍一种强化学习框架，用于在这些实验中进行A/B测试，同时表征长期治疗效果。我们建议的测试程序允许顺序监控和在线更新。它一般适用于不同行业的各种处理设计。此外，我们系统地研究了我们测试过程的理论属性(例如，大小和功率)。最后，我们将我们的框架应用于模拟数据和从一家科技公司获得的真实数据实例，以说明其相对于当前实践的优势。我们测试的PYTHON实现可在https://github.com/callmespring/CausalRL.上获得这篇文章的补充材料可以在网上找到。
Abstract 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 https://github.com/callmespring/CausalRL. Supplementary materials for this article are available online.