Abstract:
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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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