Methoden vergelijken

	Event Study Design (Causale Event Study)	Onderbroken Tijdreeks (ITS) Analyse
Vakgebied	Causale inferentie	Causale inferentie
Familie	Regression model	Regression model
Jaar van ontstaan	2021	2002
Grondlegger	Sun & Abraham (2021); Callaway & Sant'Anna (2021)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Type	Dynamic causal panel regression	Quasi-experimental segmented regression
Oorspronkelijke bron	Sun, L. & Abraham, S. (2021). Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Journal of Econometrics, 225(2), 175–199.	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.
Aliassen	dynamic difference-in-differences, event-study DiD, dynamic treatment effects, leads-and-lags model	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Verwant	5	5
Samenvatting	The event study design is a generalised difference-in-differences model that estimates a separate treatment-effect coefficient for each period before and after an intervention, tracing the dynamics of the effect over event time. Its modern, heterogeneity-robust form was developed by Sun & Abraham (2021) and Callaway & Sant'Anna (2021).	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.