A groupwise approach for inferring heterogeneous treatment effects in causal inference

There is a growing literature in nonparametric estimation of the conditional average treatment effect given a specific value of covariates. However, this estimate is often difficult to interpret if covariates are high dimensional and in practice, effect heterogeneity is discussed in terms of subgroups of individuals with similar attributes. The paper propose to study treatment heterogeneity under the groupwise framework. Our method is simple, only based on linear regression and sample splitting, and is semiparametrically efficient under assumptions. We also discuss ways to conduct multiple testing. We conclude by reanalyzing a get-out-the-vote experiment during the 2014 U.S. midterm elections.

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