Personalized Assignment to One of Many Treatment Arms via Regularized and Clustered Joint Assignment Forests
通过正则化与聚类联合分配森林实现多处理臂个性化分配
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.…