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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 #317535
Title: Personalizing Treatment Assignment Rules in Large Multi-Arm Experimental Settings
Author(s): Rahul Ladhania* and Lyle Ungar
Companies: University of Michigan and University of Pennsylvania
Keywords: optimal assignment rule; personalized treatments; welfare maximization; heterogeneous treatment effects; causal inference; machine learning

We learn personalized assignment rules from among many treatment arms from a large randomized controlled trial. We argue that a large number of treatment arms makes finding the best arm hard, while we can still achieve sizable welfare gains from personalization by direct optimization. We then document the performance of a forest-based assignment algorithm in a simulation exercise and apply it to a behavioral mega-study with more than 50 treatment arms, aimed at promoting the formation of lasting exercise habits.

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

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