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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
Abstract #317533
Title: Learning for Long-Term Outcomes
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

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.

Authors who are presenting talks have a * after their name.

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