Comparar métodos

	Policy Evaluation Counterfactual Impact Evaluation	Avaliação de Impacto Contrafactual (AIC)
Área	Inferência causal	Inferência causal
Família	Regression model	Regression model
Ano de origem	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	1970s–2000s
Autor original	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Heckman, Imbens, Rubin, and the program evaluation literature
Tipo	Quasi-experimental causal evaluation	Causal inference / program evaluation
Fonte seminal	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.
Outros nomes	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Relacionados	5	5
Resumo	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.