Σύγκριση μεθόδων

	Ανθεκτική Αντιστοίχιση Βαθμολογίας Προδιάθεσης	Αντιστοίχιση Βαθμολογίας Προδιάθεσης
Πεδίο	Αιτιακή Συμπερασματολογία	Ερευνητική Στατιστική
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	2016 (robust variance correction); 1983 (PSM foundations)	1983
Δημιουργός	Abadie & Imbens (2016) for matching-on-estimated-propensity-score with corrected variance; Rosenbaum & Rubin (1983) for PSM foundations	Paul Rosenbaum and Donald Rubin
Τύπος	Quasi-experimental matching estimator with robust inference	Method
Θεμελιώδης πηγή	Abadie, A., & Imbens, G. W. (2016). Matching on the Estimated Propensity Score. Econometrica, 84(2), 781-807.	Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55.
Εναλλακτικές ονομασίες	robust PSM, PSM with robust variance, bias-corrected PSM, matching with robust inference	PSM, propensity score weighting, covariate balance
Συναφείς	6	3
Σύνοψη	Robust Propensity Score Matching (robust PSM) is a quasi-experimental causal inference method that pairs treated and control units on their estimated probability of receiving treatment (the propensity score), then estimates the average treatment effect using variance estimators that account for the uncertainty introduced by estimating the propensity score itself. The correction, developed by Abadie and Imbens (2016), prevents misleading inference that standard bootstrap or analytic formulas produce when applied naively after matching.	Propensity score matching (PSM) is a method for reducing confounding bias in observational studies by balancing baseline characteristics between treatment groups, simulating randomization. Developed by Rosenbaum and Rubin (1983), it estimates the probability of receiving treatment given observed covariates, then matches or weights treated and control individuals with similar treatment probabilities. Widely used in medicine, epidemiology, and policy evaluation when randomized trials are infeasible or unethical, enabling estimation of treatment effects while controlling for selection bias.