2022 article
Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement Learning Framework
Shi, C., Wang, X., Luo, S., Zhu, H., Ye, J., & Song, R. (2022, March 12). JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION.
author keywords: A/B testing; Causal inference; Online experiment; Online updating; Reinforcement learning; Sequential testing
TL;DR:
A reinforcement learning framework for carrying A/B testing in these experiments, while characterizing the long-term treatment effects is introduced and systematically investigates the theoretical properties of the testing procedure.
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UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Source: Web Of Science
Added: March 28, 2022
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.