방법 비교

	정책 평가 준거영향평가 (CIE)	반사실적 영향 평가 (CIE)
분야	인과추론	인과추론
계열	Regression model	Regression model
기원 연도	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	1970s–2000s
창시자	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Heckman, Imbens, Rubin, and the program evaluation literature
유형	Quasi-experimental causal evaluation	Causal inference / program evaluation
원전	Imbens, G. W., & Rubin, D. B. (2015). Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction. Cambridge University Press. ISBN: 978-0521885881	Heckman, J. J., & Vytlacil, E. J. (2007). Econometric evaluation of social programs, Part I: Causal models, structural models and econometric policy evaluation. Handbook of Econometrics, 6B, 4779-4874.
별칭	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
관련	5	5
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.	Counterfactual Impact Evaluation is a family of causal methods that estimates the effect of an intervention by comparing what actually happened to participants with what would have happened had the intervention not taken place. Formalised in the Rubin Causal Model and extended by Heckman, Imbens and others, CIE underlies most modern program and policy evaluation practice.