방법 비교

	정책 평가 준거영향평가 (CIE)	합성 통제 방법 (SCM)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	2003–2010
창시자	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
유형	Quasi-experimental causal evaluation	Quasi-experimental causal inference
원전	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.
별칭	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
관련	5	4
요약	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.