Salīdzināt metodes

	Dizains politikas novērtēšanai notikumu pētījuma ietvaros	Pārtraukto laika sēriju (ITS) analīze
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1993-2021	2002
Autors	Andrews (1993), MacKinlay (1997); formalized for policy evaluation by Freyaldenhoven, Hansen & Shapiro (2019) and Callaway & Sant'Anna (2021)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tips	Quasi-experimental / causal inference	Quasi-experimental segmented regression
Pirmavots	Callaway, B., & Sant'Anna, P. H. C. (2021). Difference-in-differences with multiple time periods. Journal of Econometrics, 225(2), 200-230.	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.
Citi nosaukumi	event study, event-study DiD, dynamic DiD, PEESD	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Saistītās	5	5
Kopsavilkums	A policy evaluation event study design is a quasi-experimental approach that estimates causal effects of a policy by plotting treatment-period-by-period coefficients around a common event time. It extends difference-in-differences to visualize both pre-treatment parallel trends and the dynamic post-treatment evolution of the policy effect, and has become the standard credibility check in applied policy research.	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.