A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference

Recently, there has been great interest in estimating the conditional average treatment effect using flexible machine learning methods. However, in practice, investigators often have working hypotheses about effect heterogeneity across pre-defined subgroups of individuals, which we call the groupwise approach. The paper compares two modern ways to estimate groupwise treatment effects, a nonparametric approach and a semiparametric approach, with the goal of better informing practice. Specifically, we compare (a) the underlying assumptions, (b) efficiency characteristics, and (c) a way to combine the two approaches. We also discuss how to obtain cluster-robust standard errors if the study units in the same subgroups are not independent and identically distributed. We conclude by reanalyzing the National Educational Longitudinal Study of 1988.