Σύγκριση μεθόδων

	Αντιστοίχιση Βαθμολογίας Προδιάθεσης Ενισχυμένη με Μηχανική Μάθηση	Εκτίμηση Διπλής Ευστάθειας (AIPW)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2004	2005
Δημιουργός	McCaffrey, Ridgeway & Morral (2004); Westreich, Lessler & Funk (2010)	Robins & Rotnitzky; Bang & Robins
Τύπος	Causal inference / matching	Semiparametric causal estimator
Θεμελιώδης πηγή	McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9(4), 403-425.	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.
Εναλλακτικές ονομασίες	ML-PSM, boosted propensity score matching, ML-augmented PSM, nonparametric propensity score matching	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Συναφείς	6	5
Σύνοψη	Machine learning-augmented propensity score matching (ML-PSM) replaces the traditional logistic regression used to estimate propensity scores with flexible machine learning algorithms — such as gradient boosted trees, random forests, or LASSO — to better capture complex, nonlinear relationships among covariates. The resulting richer propensity scores improve covariate balance and reduce bias in the estimated average treatment effect on the treated (ATT).	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.