Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Efectos Heterogéneos del Tratamiento (CATE / Meta-Aprendices)× | Random Forest× | |
|---|---|---|
| Campo≠ | Inferencia causal | Aprendizaje automático |
| Familia≠ | Regression model | Machine learning |
| Año de origen≠ | 2018 | 2001 |
| Autor original≠ | Wager & Athey (causal forest); Künzel et al. (meta-learners) | Breiman, L. |
| Tipo≠ | Causal machine-learning framework | Ensemble (bagging of decision trees) |
| Fuente seminal≠ | Wager, S. & Athey, S. (2018). Estimation and Inference of Heterogeneous Treatment Effects using Random Forests. Journal of the American Statistical Association. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias≠ | conditional average treatment effect, CATE, meta-learners, causal forest | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Relacionados≠ | 5 | 4 |
| 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). | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateConjunto de datos ↗ |
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