A0889
Title: A reinforcement learning framework for A/B testing
Authors: Chengchun Shi - LSE (United Kingdom) [presenting]
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 the current outcome as well as future outcomes. We introduce a reinforcement learning framework for carrying out A/B testing in these experiments while characterizing the long-term treatment effects. The 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 of our testing procedure. Finally, we apply our framework to both simulated data and a real-world data example obtained from a ridesharing company to illustrate its advantage over the current practice.
