Σύγκριση μεθόδων

	Αξιολόγηση Επιπτώσεων Πολιτικής – Αξιολόγηση Αντιπαραθετικών Επιπτώσεων (CIE)	Μέθοδος Εργαλειακών Μεταβλητών (IV) για Αιτιώδη Συμπερασματολογία
Πεδίο	Αιτιακή Συμπερασματολογία	Οικονομικά της Υγείας
Οικογένεια	Regression model	Process / pipeline
Έτος προέλευσης	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	1990s (modern applications)
Δημιουργός	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Angrist & Pischke (applied econometrics); rooted in econometric theory
Τύπος	Quasi-experimental causal evaluation	Method
Θεμελιώδης πηγή	Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. ISBN: 978-0521885881	Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton: Princeton University Press.
Εναλλακτικές ονομασίες	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	IV, two-stage least squares, TSLS, causal estimation
Συναφείς	5	3
Σύνοψη	Counterfactual Impact Evaluation (CIE) for policy assessment estimates the causal effect of a public policy or programme by comparing observed outcomes of participants against a rigorously constructed counterfactual — what would have happened had the policy not existed. Rooted in the Rubin potential-outcomes framework, CIE is the standard methodology endorsed by the European Commission for evaluating research, innovation, and structural funding programmes.	Instrumental variables (IV) is an econometric method to estimate causal effects when treatment or exposure is not randomly assigned and confounding is severe or unmeasured. IV relies on a third variable (instrument) that influences treatment but does not directly affect the outcome, allowing researchers to isolate the causal effect from the noise of confounding. Developed extensively in econometrics (Angrist & Pischke, 1990s–2000s), IV methods are increasingly used in health economics and health services research to leverage natural experiments and policy changes.