Confronta i metodi

	Analisi Robusta delle Serie Storiche Interrotte	Difference-in-Differences (Diff-in-Diff)
Campo	Inferenza causale	Econometria
Famiglia	Regression model	Regression model
Anno di origine	2010s	1994
Ideatore	Bernal, Cummins & Gasparrini; Linden (robust extensions)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Tipo	Quasi-experimental segmented regression with robust inference	Causal inference / panel regression
Fonte seminale	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.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Alias	robust ITS, outlier-robust ITS, robust segmented regression, robust ITSA	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Correlati	5	5
Sintesi	Robust Interrupted Time Series Analysis is a quasi-experimental method that estimates the causal effect of a policy or intervention on an aggregate outcome over time, using segmented regression fitted with outlier-resistant or heteroskedasticity-consistent standard errors. It is widely used in health services research and public-health evaluation when the time series contains influential observations, non-constant variance, or mild autocorrelation.	Difference-in-Differences is a causal-inference method that estimates the effect of an intervention by comparing how a treatment group and a control group change over time. Made famous by Card and Krueger's 1994 minimum-wage study and developed in Angrist and Pischke's Mostly Harmless Econometrics, it isolates the treatment effect as the difference between the two groups' before-after changes.