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	Analisi di Sensibilità per la Causalità nella Ricerca sull'Educazione	Analisi delle Serie Storiche Interrotte (ITS)
Campo	Inferenza causale	Inferenza causale
Famiglia	Regression model	Regression model
Anno di origine	1983–2002	2002
Ideatore	Paul R. Rosenbaum (formal framework); applied in education research by Briggs and others	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tipo	Causal robustness / bias assessment	Quasi-experimental segmented regression
Fonte seminale	Rosenbaum, P. R. (2002). Observational Studies (2nd ed.). Springer. ISBN: 978-0387989679	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	Rosenbaum sensitivity analysis, hidden-bias sensitivity analysis, causal sensitivity analysis, SA for causal education studies	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Correlati	6	5
Sintesi	Sensitivity analysis for causality in education research tests how robust a quasi-experimental finding is to unmeasured confounding. Rather than assuming all bias has been removed, it quantifies how large a hidden bias would need to be to overturn a causal conclusion — a critical safeguard when randomisation is impossible, which is common in educational settings.	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.