A Groupwise Approach for Inferring Heterogeneous Treatment Effects in Causal Inference

Understand

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 study units, 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 and adaption to the underlying data generating models, and (c) a way to combine the two approaches.