Learning for Long-Term Outcomes

• Activity Number: 284 - Learning Individualized/Sub-Group Treatment Rules in Complex Settings
• Type: Topic-Contributed
• Date/Time: Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
• Sponsor: Health Policy Statistics Section
• Author(s): Jeremy Yang and Dean Eckles and Paramveer Dhillon* and Sinan Aral
• Companies: MIT and Massachusetts Institute of Technology and University of Michigan and MIT
• Keywords: Off-policy evaluation; Policy Optimization; Surrogate index

Abstract

Decision-makers often want to target interventions (e.g., marketing campaigns) so as to maximize an outcome that is observed only in the long-term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we build on the statistical surrogacy and off-policy learning literature to impute the missing long-term outcomes and then approximate the optimal targeting policy on the imputed outcomes via a doubly-robust approach. We apply our approach in large-scale proactive churn management experiments at The Boston Globe by targeting optimal discounts to its digital subscribers to maximize their long-term revenue. We first show that conditions for validity of average treatment effect estimation with imputed outcomes are also sufficient for valid policy evaluation and optimization; furthermore, these conditions can be somewhat relaxed for policy optimization. We then validate this approach empirically by comparing it with a policy learned on the ground truth long-term outcomes and show that they are statistically indistinguishable.