Σύγκριση μεθόδων

	Ανάλυση Ετερογενούς Επίδρασης Θεραπείας με Αντιστοίχιση Βαθμολογίας Προδιάθεσης	Εκτίμηση Διπλής Ευστάθειας (AIPW)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1983–2016	2005
Δημιουργός	Rosenbaum & Rubin (PSM foundation, 1983); Athey & Imbens (HTE extensions, 2016)	Robins & Rotnitzky; Bang & Robins
Τύπος	Causal inference / matching with effect heterogeneity	Semiparametric causal estimator
Θεμελιώδης πηγή	Athey, S., & Imbens, G. W. (2016). Recursive Partitioning for Heterogeneous Causal Effects. Proceedings of the National Academy of Sciences, 113(27), 7353-7360.	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	HTE-PSM, CATE via PSM, subgroup treatment effect matching, conditional average treatment effect matching	AIPW, augmented inverse probability weighting, doubly robust estimator, Çift Gürbüz Kestirici (Augmented IPW / AIPW)
Συναφείς	5	5
Σύνοψη	Heterogeneous Treatment Effect Propensity Score Matching extends standard PSM to estimate how treatment effects vary across subgroups or individual characteristics. Rather than reporting a single average treatment effect, it uses the matched sample to estimate conditional average treatment effects (CATE), revealing which types of units benefit most or least from a treatment.	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.