Σύγκριση μεθόδων

	Εκτιμητής Αντιστοίχισης Αξιολόγησης Πολιτικής	Σχεδιασμός Ασυγχώνιστης Παλινδρόμησης (Regression Discontinuity Design - RDD)
Πεδίο	Αιτιακή Συμπερασματολογία	Αιτιακή Συμπερασματολογία
Οικογένεια	Regression model	Regression model
Έτος προέλευσης	1998-2006	2008
Δημιουργός	Heckman, Ichimura & Todd; Abadie & Imbens	Imbens & Lemieux (guide to practice); Cattaneo, Idrobo & Titiunik (practical introduction)
Τύπος	Non-parametric causal estimator	Quasi-experimental causal design
Θεμελιώδης πηγή	Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235-267.	Imbens, G. W., & Lemieux, T. (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142(2), 615-635.
Εναλλακτικές ονομασίες	matching estimator, program evaluation matching, treatment effect matching, Abadie-Imbens estimator	RDD, regression discontinuity design, sharp RDD, fuzzy RDD
Συναφείς	6	5
Σύνοψη	The policy evaluation matching estimator estimates the causal effect of a program or policy on treated units by pairing each participant with one or more non-participants who share similar pre-treatment characteristics. Developed rigorously by Heckman, Ichimura & Todd (1998) and Abadie & Imbens (2006), it avoids parametric outcome models and is the standard non-parametric tool for program and policy evaluation.	Regression Discontinuity Design is a quasi-experimental method that identifies a causal effect by locally comparing units just above and just below a cutoff on a continuous assignment (running) variable. Formalised for applied work by Imbens and Lemieux (2008) and developed as a practical framework by Cattaneo, Idrobo, and Titiunik (2020), it estimates a local average treatment effect (LATE) at the threshold.