Methoden vergelijken

	Multi-period Interrupted Time Series	Difference-in-Differences (DiD)
Vakgebied	Causale inferentie	Econometrie
Familie	Regression model	Regression model
Jaar van ontstaan	2000s-2015	1994
Grondlegger	Extended from segmented regression / ITS tradition; multi-break formalization developed across epidemiology and health policy literature (2000s-2010s)	Card & Krueger (canonical 1994 application); Angrist & Pischke (textbook treatment)
Type	Quasi-experimental time series regression	Causal inference / panel regression
Oorspronkelijke bron	Kontopantelis, E., Doran, T., Springate, D. A., Buchan, I., & Reeves, D. (2015). Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis. BMJ, 350, h2750.	Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press. ISBN: 978-0691120355
Aliassen	multi-period ITS, multiple-interruption ITS, segmented time series with multiple breakpoints, MITS	diff-in-diff, DiD, Farkların Farkı (Diff-in-Diff)
Verwant	5	5
Samenvatting	Multi-period Interrupted Time Series (MITS) extends the classic ITS framework to settings where two or more interventions occur at known time points within the same series. By fitting a segmented regression with multiple breakpoints, MITS estimates the level change and slope change attributable to each intervention while controlling for the underlying secular trend and for the effects of earlier interruptions.	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.