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 heterogeneity at the subgroup level is more intuitive and intelligble from a decision-making perspective. In this paper, we provide a tree-based method, called the generic causal tree (GCT), to identify the subgroup-level treatment effects in observational studies. The tree is designed to split 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 honest estimation to separate tree-building process and inference process. In the simulation, we show that the GCT algorithm has distinct advantages in subgroup identification and gives estimation with higher accuracy compared with the other two benchmark methods. Additionally, we verify the effectiveness of statistical inference by GCT.