Inference of subgroup-level treatment effects via generic causal tree in observational studies

Abstract

Exploring heterogeneity in causal effects has wide applications in the field of policy evaluation and decision-making. In recent years, researchers have begun employing machine learning methods to study causality, among which the most popular methods generally estimate heterogeneous treatment effects at the individual level. However, we argue that in large sample cases, identifying specific subgroups with heterogeneity will be more intuitive and intelligible for decision-making. In this paper, we provide a tree-based method, called “Generic Causal Tree” (GCT), to identify the subgroup-level treatment effects in observational studies. The tree is designed to grow by maximizing the disparity of treatment effects between subgroups, embedding a semiparametric framework for the improvement of treatment effect estimation. To accomplish valid statistical inference of the tree-based estimators of treatment effects, we adopt the honest estimation to separate tree-building and inference. In the simulation, we show that the Generic Causal Tree algorithm has distinct advantages in subgroup identification and gives estimation with higher accuracy compared with the other two benchmark methods. Also, we verify the effectiveness of statistical inference by Generic Causal Tree.