Estimating heterogeneous treatment effect by balancing heterogeneity and fitness

Estimating heterogeneous treatment effect is a fundamental problem in biological and medical applications. Recently, several recursive partitioning methods have been proposed to identify the subgroups that respond differently towards a treatment, and they rely on a fitness criterion to minimize the error between the estimated treatment effects and the unobservable ground truths.

In this paper, we propose that a heterogeneity criterion, which maximizes the differences of treatment effects among the subgroups, also needs to be considered. Moreover, we show that better performances can be achieved when the fitness and the heterogeneous criteria are considered simultaneously. Selecting the optimal splitting points then becomes a multi-objective problem; however, a solution that achieves optimal in both aspects are often not available. To solve this problem, we propose a multi-objective splitting procedure to balance both criteria. The proposed procedure is computationally efficient and fits naturally into the existing recursive partitioning framework. Experimental results show that the proposed multi-objective approach performs consistently better than existing ones.

Heterogeneity should be considered with fitness in heterogeneous treatment effect estimation, and the proposed multi-objective splitting procedure achieves the best performance by balancing both criteria.