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 and adaption to the underlying data generating models, and (c) a way to combine the two approaches. We also discuss how to test a key assumption concerning the semiparametric estimator and to obtain cluster-robust standard errors if individuals in the same subgroups are not independent and identically distributed. We conclude by reanalyzing the Early Childhood Longitudinal Study.

翻译：近年来，利用灵活的机器学习方法估计条件平均处理效应引起了广泛关注。然而在实践中，研究者通常会对预定义个体子组间的效应异质性提出工作假设，我们称之为分组方法。本文比较了估计分组处理效应的两种现代方法——非参数方法和半参数方法，旨在更好地指导实践应用。具体而言，我们比较了：(a) 基本假设条件；(b) 对潜在数据生成模型的效率与适应性；(c) 两种方法的结合方式。此外，我们还讨论了如何检验半参数估计器的关键假设，以及在同子组内个体非独立同分布情况下如何获得聚类稳健标准误。最后，我们通过对早期儿童纵向研究的重新分析得出结论。