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摘要翻译：
我们研究了因果机器学习估计器在不同聚集水平下对异构因果效应的有限样本性能。我们采用了一项经验蒙特卡罗研究，它依赖于基于实际数据的可以说是现实的数据生成过程(DGPs)。我们考虑了24种不同的DGPs、11种不同的因果机器学习估计器和估计效果的三个聚合水平。在主要的DGPs中，我们允许基于丰富的可观察协变量集选择治疗。我们给出了估计量可以分为三组的证据。第一组在所有DGPs和聚合级别上都表现良好。这些评估器有多个步骤来说明治疗和结果过程中的选择。第二组只对特定的DGPS显示有竞争力的表现。第三组明显优于其他估计者。
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英文标题：
《Machine Learning Estimation of Heterogeneous Causal Effects: Empirical
Monte Carlo Evidence》
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作者：
Michael C. Knaus, Michael Lechner, Anthony Strittmatter
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最新提交年份：
2018
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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. We consider 24 different DGPs, eleven different causal machine learning estimators, and three aggregation levels of the estimated effects. In the main DGPs, we allow for selection into treatment based on a rich set of observable covariates. We provide evidence that the estimators can be categorized into three groups. The first group performs 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. The second group shows competitive performance only for particular DGPs. The third group is clearly outperformed by the other estimators.
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PDF链接：
https://arxiv.org/pdf/1810.13237
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