Comparar métodos

	Evaluación Robusta de Impacto Contrafactual	Evaluación de Impacto Contrafactual (EIC)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2010s	1970s–2000s
Autor original	European Commission evaluation community; Pellegrini, Ferrara and colleagues	Heckman, Imbens, Rubin, and the program evaluation literature
Tipo	Robustness-validated causal evaluation	Causal inference / program evaluation
Fuente seminal	Bia, M., Flores, C. A., Flores-Lagunes, A., & Mattei, A. (2014). A Stata package for the application of semiparametric estimators of dose–response functions. Stata Journal, 14(3), 580–604.	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	Robust CIE, Sensitivity-checked CIE, Multi-method counterfactual evaluation, Robustness-validated impact evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Relacionados	5	5
Resumen	Robust Counterfactual Impact Evaluation (Robust CIE) strengthens causal impact estimates by combining multiple quasi-experimental estimators, placebo tests, and formal sensitivity analyses. Rather than relying on a single method, it cross-validates findings across approaches — such as matching, difference-in-differences, and regression discontinuity — to ensure that conclusions do not depend on any single methodological choice.	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.