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. We also discuss how to test a key assumption concerning the semiparametric estimator and to obtain cluster-robust standard errors if study units in the same subgroups are correlated. We demonstrate our findings by conducting simulation studies and 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兴趣。然而在实践中，研究者通常对研究单元预先定义的分组间的效应异质性持有工作假设，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——非参数方法与半参数方法，旨在为实践提供更好指导。具体而言，我们比较了：(a) 基本假设；(b) 对潜在数据生成模型的适应性与效率；以及(c) 两种方法的结合方式。我们还讨论了如何检验半参数估计量的关键假设，以及当同一组内研究单元存在相关性时如何获取聚类稳健标准误。通过模拟研究和对早期儿童纵向研究的重新分析，我们展示了研究结论。