Methoden vergelijken

	Robuuste Causale Impactevaluatie	Counterfactual Impact Evaluation (CIE)
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2010s	1970s–2000s
Grondlegger	European Commission evaluation community; Pellegrini, Ferrara and colleagues	Heckman, Imbens, Rubin, and the program evaluation literature
Type	Robustness-validated causal evaluation	Causal inference / program evaluation
Oorspronkelijke bron	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.
Aliassen	Robust CIE, Sensitivity-checked CIE, Multi-method counterfactual evaluation, Robustness-validated impact evaluation	CIE, counterfactual evaluation, counterfactual policy evaluation, impact evaluation
Verwant	5	5
Samenvatting	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.