Comparar métodos

	Evaluación de Impacto Contrafactual en Investigación Educativa	Análisis de Series Temporales Interrumpidas (ITS)
Campo	Inferencia causal	Inferencia causal
Familia	Regression model	Regression model
Año de origen	2000s–2010s	2002
Autor original	Blundell & Costa Dias; formalized for EU education policy by the European Commission Joint Research Centre	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tipo	Quasi-experimental causal inference framework	Quasi-experimental segmented regression
Fuente seminal	Blundell, R., & Costa Dias, M. (2002). Alternative approaches to evaluation in empirical microeconomics. Portuguese Economic Journal, 1(2), 91-115.	Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355.
Alias	CIE in education, counterfactual program evaluation, causal impact evaluation, education policy impact evaluation	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Relacionados	5	5
Resumen	Counterfactual impact evaluation (CIE) is the systematic application of causal inference designs — such as difference-in-differences, regression discontinuity, matching, and instrumental variables — to measure the genuine effect of education programs, policies, or interventions by constructing a credible counterfactual: what would have happened to participants had they not been treated.	Interrupted Time Series analysis is a quasi-experimental design that estimates the effect of a single, well-dated intervention by comparing the trajectory of an outcome before and after it occurs. Formalised as segmented regression by Wagner and colleagues (2002) and popularised as a public-health evaluation tutorial by Bernal, Cummins and Gasparrini (2017), it separates the intervention's impact into a change in level and a change in slope.