标题： 通过正则化和聚类联合分配森林对多个治疗组中的一个进行个性化分配 显示英文标题

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

摘要： 我们考虑从随机对照试验中学习对多个处理组之一的个性化分配。 在这种情况下的标准方法，可能由于方差过大而表现不佳，因为它们分别估计每个组的异质处理效应。 我们则提出了一些在处理组之间共享信息的方法：首先，我们考虑一种基于贪婪递归分块的正则化森林分配算法，该算法在各组之间收缩效应估计。 其次，我们通过一种聚类方案来增强我们的算法，该方案结合了结果一致相似的处理组。 在模拟研究中，我们将这些方法的性能与单独预测各组结果的方法进行比较，并记录了通过正则化和聚类直接优化处理分配所带来的优势。 在一个理论模型中，我们说明了处理组数量较多会使找到最佳组变得困难，而通过正则化优化可以实现显著的效用提升。 显示英文摘要

摘要： We consider learning personalized assignments to one of many treatment arms from a randomized controlled trial. Standard methods that estimate heterogeneous treatment effects separately for each arm may perform poorly in this case due to excess variance. We instead propose methods that pool information across treatment arms: First, we consider a regularized forest-based assignment algorithm based on greedy recursive partitioning that shrinks effect estimates across arms. Second, we augment our algorithm by a clustering scheme that combines treatment arms with consistently similar outcomes. In a simulation study, we compare the performance of these approaches to predicting arm-wise outcomes separately, and document gains of directly optimizing the treatment assignment with regularization and clustering. In a theoretical model, we illustrate how a high number of treatment arms makes finding the best arm hard, while we can achieve sizable utility gains from personalization by regularized optimization.