方法对比
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| 异质性处理效应（CATE / 元学习器）× | 工具变量法/两阶段最小二乘法 (IV/2SLS)× | |
|---|---|---|
| 领域 | 因果推断 | 因果推断 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2018 | 2009 |
| 提出者≠ | Wager & Athey (causal forest); Künzel et al. (meta-learners) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| 类型≠ | Causal machine-learning framework | Instrumental-variables regression |
| 开创性文献≠ | Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. DOI ↗ | Angrist, J. D. & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355 |
| 别名≠ | conditional average treatment effect, CATE, meta-learners, causal forest | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| 相关 | 5 | 5 |
| 摘要≠ | Heterogeneous Treatment Effects is a machine-learning framework that estimates how a treatment effect varies across individuals — the conditional average treatment effect (CATE). It bundles meta-learner strategies such as the T-Learner, S-Learner, X-Learner and R-Learner alongside the causal forest of Wager and Athey (2018) and Künzel et al. (2019). | IV/2SLS is a two-stage estimation method that recovers the causal effect of an endogenous regressor by isolating the part of its variation driven by an external instrument. It is the workhorse identification strategy in modern applied econometrics, developed at length in Angrist and Pischke's Mostly Harmless Econometrics (2009). |
| ScholarGate数据集 ↗ |
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