Comparar métodos

	Evaluación de Impacto Contrafactual (CIE) para la Evaluación de Políticas	Método de Control Sintético (SCM)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	1974 (Rubin potential outcomes); 2010s (EU policy CIE formalisation)	2003–2010
Autor original	Rubin (potential outcomes framework); European Commission DG Research formalised policy CIE guidelines	Alberto Abadie & Javier Gardeazabal (2003); Abadie, Diamond & Hainmueller (2010)
Tipo	Quasi-experimental causal evaluation	Quasi-experimental causal inference
Fuente seminal	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.
Alias	CIE, policy CIE, counterfactual policy evaluation, impact evaluation	SCM, synthetic control, synth estimator, Abadie-Diamond-Hainmueller method
Relacionados	5	4
Resumen	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.