| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP13402 |
| DP13402 Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence | |
| Michael Lechner; Anthony Strittmatter | |
| 发表日期 | 2018-12-18 |
| 出版年 | 2018 |
| 语种 | 英语 |
| 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. |
| 主题 | Labour Economics |
| 关键词 | Causal machine learning Conditional average treatment effects Selection-on-observables Random forest Causal forest Lasso |
| URL | https://cepr.org/publications/dp13402 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/542212 |
| 推荐引用方式 GB/T 7714 | Michael Lechner,Anthony Strittmatter. DP13402 Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence. 2018. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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