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

Shi, Chengchun ORCID: 0000-0001-7773-2099, Wang, Xiaoyu, Luo, Shikai, Zhu, Hongtu, Ye, Jieping and Song, Rui (2022) Dynamic causal effects evaluation in A/B testing with a reinforcement learning framework. Journal of the American Statistical Association. 1 - 13. ISSN 0162-1459

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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.

Item Type:	Article
Official URL:	https://www.tandfonline.com/toc/uasa20/current
Additional Information:	© 2022 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	04 Jan 2022 16:57
Last Modified:	16 Nov 2024 07:21
URI:	http://eprints.lse.ac.uk/id/eprint/113310

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