Salīdzināt metodes

	Pārtrauktu laika sēriju politikas novērtēšanai	Pārtraukto laika sēriju (ITS) analīze
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)	2002
Autors	Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)	Wagner, Soumerai, Zhang & Ross-Degnan (segmented regression); Bernal, Cummins & Gasparrini (tutorial)
Tips	Quasi-experimental causal design	Quasi-experimental segmented regression
Pirmavots	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.	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	ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS	ITS analysis, segmented regression of time series, Kesintili Zaman Serisi (ITS) Analizi
Saistītās	4	5
Kopsavilkums	Interrupted Time Series (ITS) for policy evaluation uses routinely collected aggregate time-series data to estimate the causal impact of a policy change. A segmented regression model splits the series at a known intervention date, estimating both an immediate level shift and a change in trend attributable to the policy — without requiring a randomised control group.	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.