Comparar métodos

	Evaluación Robusta de Impacto Contrafactual	Análisis de sensibilidad para la causalidad
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2010s	1983–2002
Autor original	European Commission evaluation community; Pellegrini, Ferrara and colleagues	Paul R. Rosenbaum (hidden-bias framework); extended by Cinelli & Hazlett (omitted-variable approach)
Tipo	Robustness-validated causal evaluation	Diagnostic / robustness check
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.	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679
Alias	Robust CIE, Sensitivity-checked CIE, Multi-method counterfactual evaluation, Robustness-validated impact evaluation	sensitivity analysis, hidden-bias sensitivity analysis, Rosenbaum sensitivity analysis, omitted-variable sensitivity
Relacionados	5	4
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.	Sensitivity analysis for causality assesses how robust a causal conclusion is to unobserved confounding. Rather than assuming all confounders are controlled, it asks: how strong would an unmeasured variable need to be to overturn the estimated effect? It is an indispensable robustness check after any quasi-experimental or observational causal analysis.