Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Estimation doublement robuste (AIPW)× | Forêt Aléatoire× | |
|---|---|---|
| Domaine≠ | Inférence causale | Apprentissage automatique |
| Famille≠ | Regression model | Machine learning |
| Année d'origine≠ | 2005 | 2001 |
| Auteur d'origine≠ | Robins & Rotnitzky; Bang & Robins | Breiman, L. |
| Type≠ | Semiparametric causal estimator | Ensemble (bagging of decision trees) |
| Source fondatrice≠ | Robins, J. M. & Rotnitzky, A. (1995). Semiparametric Efficiency in Multivariate Regression Models with Missing Data. Journal of the American Statistical Association, 90(429), 122-129. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW) | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Apparentées≠ | 5 | 4 |
| Résumé≠ | Doubly Robust Estimation, also called Augmented Inverse Probability Weighting (AIPW), is a semiparametric method for estimating causal treatment effects that combines an outcome regression model with a propensity (treatment) model. Developed in the work of Robins & Rotnitzky (1995) and Bang & Robins (2005), it stays consistent as long as at least one of the two models is correctly specified. | 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. |
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