Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Efectos Heterogéneos del Tratamiento (CATE / Meta-Aprendices)× | Variables Instrumentales mediante Mínimos Cuadrados en Dos Etapas (IV/2SLS)× | |
|---|---|---|
| Campo | Inferencia causal | Inferencia causal |
| Familia | Regression model | Regression model |
| Año de origen≠ | 2018 | 2009 |
| Autor original≠ | 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 |
| Fuente seminal≠ | 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 |
| Relacionados | 5 | 5 |
| Resumen≠ | 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). |
| ScholarGateConjunto de datos ↗ |
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