Online Causal Inference Seminar

Free and open to the public

Tuesday, March 15, 2022 [Link to join] (ID: 996 2837 2037, Password: 386638)

• Speaker: Chengchun Shi (LSE)
• Title: A reinforcement learning framework for dynamic causal effects evaluation in A/B testing
• Discussant: Will Wei Sun (Purdue University)
• 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. In this talk, we 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 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.