估计员
计算机科学
蒙特卡罗方法
观察研究
样品(材料)
结果(博弈论)
因果推理
计量经济学
人工智能
机器学习
统计
数学
色谱法
数理经济学
化学
作者
Michael C. Knaus,Michael Lechner,Anthony Strittmatter
摘要
Summary 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.
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