In this paper, we introduce the causal forests method (Athey et al., 2019) and illustrate how to apply it in social sciences to addressing treatment effect heterogeneity. Compared with existing parametric methods such as the multiplicative interaction model and traditional semi-/non-parametric estimation, causal forests are more flexible for complex data generating processes. Specifically, causal forests allow for nonparametric estimation and inference on heterogeneous treatment effects in the presence of many moderators. To reveal its usefulness, we revisit existing studies in political science and economics. We uncover new information hidden by original estimation strategies while producing findings that are consistent with conventional methods. Through these replication efforts, we provide a step-by-step practice guide for applying causal forests in evaluating treatment effect heterogeneity.