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| Effetti Eterogenei del Trattamento (CATE / Meta-Learner)× | Variabili Strumentali tramite Minimi Quadrati a Due Stadi (IV/2SLS)× | |
|---|---|---|
| Campo | Inferenza causale | Inferenza causale |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2018 | 2009 |
| Ideatore≠ | Wager & Athey (causal forest); Künzel et al. (meta-learners) | Angrist & Pischke (textbook treatment); Stock & Yogo (weak-instrument theory) |
| Tipo≠ | Causal machine-learning framework | Instrumental-variables regression |
| Fonte seminale≠ | 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 |
| Alias≠ | conditional average treatment effect, CATE, meta-learners, causal forest | instrumental variables, IV estimation, 2SLS, instrumental variable regression |
| Correlati | 5 | 5 |
| Sintesi≠ | 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). |
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