It is valuable for any decision maker to know the impact of decisions (treatments) on average and for subgroups. The causal machine learning literature has recently provided tools for estimating group average treatment effects (GATE) to understand treatment heterogeneity better. This paper addresses the challenge of interpreting such differences in treatment effects between groups while accounting for variations in other covariates. We propose a new parameter, the balanced group average treatment effect (BGATE), which measures a GATE with a specific distribution of a priori-determined covariates. By taking the difference of two BGATEs, we can analyse heterogeneity more meaningfully than by comparing two GATEs. The estimation strategy for this parameter is based on double/debiased machine learning for discrete treatments in an unconfoundedness setting, and the estimator is shown to be $\sqrt{N}$-consistent and asymptotically normal under standard conditions. Adding additional identifying assumptions allows specific balanced differences in treatment effects between groups to be interpreted causally, leading to the causal balanced group average treatment effect. We explore the finite sample properties in a small-scale simulation study and demonstrate the usefulness of these parameters in an empirical example.

翻译：对于任何决策者而言，了解决策（处理）对整体及子群体的影响都具有重要价值。因果机器学习领域近期提供了估计群体平均处理效应（GATE）的工具，以更深入地理解处理异质性。本文针对在考虑其他协变量变化的情况下，解释不同群体间处理效应差异这一挑战。我们提出新参数——平衡群体平均处理效应（BGATE），该参数用于衡量具有特定先验确定协变量分布的GATE。通过计算两个BGATE的差值，我们能够比直接比较两个GATE更有意义地分析异质性。该参数的估计策略基于非混淆假设下针对离散处理的双重/去偏机器学习，且该估计量在标准条件下被证明具有$\sqrt{N}$-一致性和渐近正态性。通过增加额外识别假设，不同群体间处理效应的特定平衡差异可被因果解释，进而形成因果平衡群体平均处理效应。我们通过小规模仿真研究探索了有限样本性质，并利用实证案例展示了这些参数的实际应用价值。