Methoden vergelijken

	Panel Data Interrupted Time Series	Difference-in-Differences (DiD)
Vakgebied	Causale inferentie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	2000s–2010s	1994
Grondlegger	Shadish, Cook & Campbell (design framework); Bernal, Cummins & Gasparrini (epidemiological tutorial)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Quasi-experimental causal inference	Causal inference / panel regression
Oorspronkelijke bron	Lopez Bernal, J., 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
Aliassen	panel ITS, multi-unit ITS, panel ITSA, controlled interrupted time series	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwant	5	5
Samenvatting	Panel Data Interrupted Time Series (panel ITS) is a quasi-experimental method that estimates the causal effect of an intervention using repeated observations from multiple units over time. By exploiting variation across both units and time periods, it provides stronger causal identification than single-unit ITS, detecting changes in the level and slope of the outcome trajectory immediately following a clearly dated intervention.	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.