Dynamic causal effects evaluation in A/B testing with a reinforcement learning framework

Chengchun Shi, Xiaoyu Wang, Shikai Luo, Hongtu Zhu, Jieping Ye and Rui Song

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: A/B testing; online experiment; reinforcement learning; causal inference; sequential testing; online updating; Research Support Fund; NSF-DMS-1555244; NSF-DMS-2113637 (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 13 pages
Date: 2022-01-20
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-exp and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Journal of the American Statistical Association, 20, January, 2022, pp. 1 - 13. ISSN: 0162-1459

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