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Activity Number: 169 - Estimating Heterogeneity in Treatment Effects in Complex Real World Settings
Type: Topic Contributed
Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 11:50 AM
Sponsor: Health Policy Statistics Section
Abstract #313703
Title: WITHDRAWN Personalizing Treatments for Habit Formation: Learning Optimal Treatment Rules from a Multi-Arm Experiment
Author(s): Rahul Ladhania
Keywords: causal inference; optimal assignment; multiple treatments; heterogeneous treatment effects; individualized treatment rule; behavioral economics

In this study, we learn the optimal treatment assignment rule in a setting with a large number of discrete treatment arms. We propose a new recursive partitioning tree and forest based approach in a multiple treatment setting to learn and validate the individualized assignment rules. We apply this method to data from a real-world ‘mega’ randomized control trial conducted in collaboration with a national gym chain, with multiple behavioral interventions promoting the formation of lasting exercise habits. We compare our method to existing classification-based outcome weighted learning approaches.

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