Σύγκριση μεθόδων

	Εκτιμητής Στιβαρού Ταίριασματος (Διόρθωση Μεροληψίας κατά το Ταίριασμα)	Αντίστροφη Πιθανότητα Στάθμισης Θεραπείας (IPW / IPTW)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	2006/2011	2000
Δημιουργός	Abadie & Imbens	Robins, Hernán & Brumback
Τύπος	Causal inference / matching	Causal inference weighting estimator
Θεμελιώδης πηγή	Abadie, A., & Imbens, G. W. (2011). Bias-Corrected Matching Estimators for Average Treatment Effects. Journal of Business & Economic Statistics, 29(1), 1-11.	Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal Structural Models and Causal Inference in Epidemiology. Epidemiology, 11(5), 550-560.
Εναλλακτικές ονομασίες	bias-corrected matching, Abadie-Imbens matching, AI matching estimator, robust nearest-neighbor matching	IPW, IPTW, inverse probability of treatment weighting, marginal structural model weighting
Συναφείς	6	5
Σύνοψη	The robust matching estimator, developed by Abadie and Imbens (2006, 2011), extends nearest-neighbor matching by adding a regression-based bias correction that removes the finite-sample bias arising when matched units are not perfectly alike. It yields consistent, asymptotically normal estimates of average treatment effects with a heteroskedasticity-robust variance formula that is valid regardless of the number of continuous covariates.	Inverse Probability Weighting is a causal-inference method that assigns each observation a weight equal to the inverse of its probability of receiving the treatment it actually received. Introduced by Robins, Hernán and Brumback (2000) for marginal structural models, it builds a pseudo-population in which treatment is independent of measured confounders, balancing selection bias.