| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP13430 |
| DP13430 Modified Causal Forests for Estimating Heterogeneous Causal Effects | |
| Michael Lechner | |
| 发表日期 | 2019-01-06 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | Uncovering the heterogeneity of causal effects of policies and business decisions at various levels of granularity provides substantial value to decision makers. This paper develops new estimation and inference procedures for multiple treatment models in a selection-on-observables framework by modifying the Causal Forest approach suggested by Wager and Athey (2018). The new esti-mators have desirable theoretical and computational properties for various aggregation levels of the causal effects. An Empirical Monte Carlo study shows that they may outperform previously suggested estimators. Inference tends to be accurate for effects relating to larger groups and conservative for effects relating to fine levels of granularity. An application to the evaluation of an active labour mar-ket programme shows the value of the new methods for applied research. |
| 主题 | Labour Economics |
| 关键词 | Causal machine learning Statistical learning Average treatment effects Conditional aver-age treatment effects Multiple treatments Selection-on-observable Causal forests |
| URL | https://cepr.org/publications/dp13430 |
| 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/542246 |
| 推荐引用方式 GB/T 7714 | Michael Lechner. DP13430 Modified Causal Forests for Estimating Heterogeneous Causal Effects. 2019. |
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