Learning When-to-Treat Policies

• Activity Number: 135 - Move Non/Semiparametrics Forward in Causal Inference, Missing Data Analysis, and Data Integration
• Type: Invited
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Stefan Wager and Xinkun Nie* and Emma Brunskill
• Companies: Stanford University and Stanford University and Stanford University

Abstract

Many applied decision-making problems have a dynamic component: The policymaker needs not only to choose whom to treat, but also when to start which treatment. For example, a medical doctor may see a patient many times and, at each visit, need to choose between prescribing either an invasive or a non-invasive procedure and postponing the decision to the next visit. In this paper, we develop an "advantage doubly robust" estimator for learning such dynamic treatment rules using observational data under sequential ignorability. We prove welfare regret bounds that generalize results for doubly robust learning in the single-step setting, and show promising empirical performance in several different contexts. Our approach is practical for policy optimization, and does not need any structural (e.g., Markovian) assumptions.