An analysis of heterogeneous treatment effects: new opportunities and challenges with machine learning techniques

Abstract The investigation of heterogeneous treatment effects is a current focus for many empirical sociologists. This article considers causal random forests and Bayesian additive regression trees as methods to demonstrate how algorithmic approaches potentially transcend conventional model-form and covariate-selection restrictions, and can examine complex interactions between the treatment effect and covariates. These two methods, respectively, illustrate the ideas of “matching” and “simulation”, and provide estimates of the individual treatment effect. This enables scholars to examine the empirical distribution of treatment effects and investigate the determinants of their heterogeneity. However, algorithm-based methods can also pose new challenges. For instance, arbitrariness in parameter configuration and algorithm variation can undermine the consistency of empirical results.