Compară metode

	Evaluarea Impactului Contrafactual (EIC) pentru Politici Publice	Metoda Controlului Sintetic (MCS)
Domeniu	Inferență cauzală	Inferență cauzală
Familie	Regression model	Regression model
Anul apariției	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	2003–2010
Autorul original	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tip	Quasi-experimental causal evaluation	Quasi-experimental causal inference
Sursa seminală	Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. ISBN: 978-0521885881	Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493-505.
Denumiri alternative	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Înrudite	5	4
Rezumat	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.	The Synthetic Control Method estimates the causal effect of a treatment or policy on a single treated unit by constructing a weighted combination of untreated units — the synthetic control — that closely resembles the treated unit before the intervention. The gap between the treated unit and its synthetic counterpart after the intervention is the estimated treatment effect.