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We consider learning personalized assignments to one of many treatment armsfrom a randomized controlled trial. Standard methods that estimateheterogeneous treatment effects separately for each arm may perform poorly inthis case due to excess variance. We instead propose methods that poolinformation across treatment arms: First, we consider a regularizedforest-based assignment algorithm based on greedy recursive partitioning thatshrinks effect estimates across arms. Second, we augment our algorithm by aclustering scheme that combines treatment arms with consistently similaroutcomes. In a simulation study, we compare the performance of these approachesto predicting arm-wise outcomes separately, and document gains of directlyoptimizing the treatment assignment with regularization and clustering. In atheoretical model, we illustrate how a high number of treatment arms makesfinding the best arm hard, while we can achieve sizable utility gains frompersonalization by regularized optimization.

Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests