We investigate the finite-sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an empirical Monte Carlo study that relies on arguably realistic data generation processes (DGPs) based on actual data in an observational setting. We consider 24 DGPs, eleven causal machine learning estimators, and three aggregation levels of the estimated effects. Four of the considered estimators perform consistently well across all DGPs and aggregation levels. These estimators have multiple steps to account for the selection into the treatment and the outcome process.

中文翻译：

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机器学习对异类因果影响的估计：经验蒙特卡洛证据

我们调查因果机器学习估计量在不同聚合水平上的因果因果效应的有限样本性能。我们采用了经验性的蒙特卡洛研究，该研究依赖可观的现实数据生成过程（DGP），该过程基于观测环境中的实际数据。我们考虑了24个DGP，11个因果机器学习估计器以及估计效果的三个汇总级别。在所有DGP和汇总级别中，四个被考虑的估算器始终表现良好。这些估算器有多个步骤来说明对治疗方法和结果过程的选择。