Comparer des méthodes

	Évaluation d'Impact Contrefactuel (EIC) pour l'Évaluation des Politiques	Évaluation d'Impact Contrefactuel (EIC)
Domaine	Inférence causale	Inférence causale
Famille	Regression model	Regression model
Année d'origine	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	1970s–2000s
Auteur d'origine	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Heckman, Imbens, Rubin, and the program evaluation literature
Type	Quasi-experimental causal evaluation	Causal inference / program evaluation
Source fondatrice	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.
Alias	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Apparentées	5	5
Résumé	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.