Salīdzināt metodes

	Pārtrauktu laika sēriju politikas novērtēšanai	Politikas novērtējums ar "starpību starp starpībām" metodi
Nozare	Cēloņsakarību secināšana	Cēloņsakarību secināšana
Saime	Regression model	Regression model
Izcelsmes gads	1975 (intervention analysis); 2000s–2010s (policy evaluation framing)	1978-2009
Autors	Box & Tiao (1975); popularised for policy by Shadish, Cook & Campbell (2002) and Bernal et al. (2017)	Ashenfelter (1978); Heckman, LaLonde & Smith (1999); Imbens & Wooldridge (2009)
Tips	Quasi-experimental causal design	Quasi-experimental / policy evaluation
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.	Imbens, G. W., & Wooldridge, J. M. (2009). Recent Developments in the Econometrics of Program Evaluation. Journal of Economic Literature, 47(1), 5-86.
Citi nosaukumi	ITS for policy evaluation, policy ITS, segmented regression for policy, policy impact ITS	policy DiD, program evaluation DiD, policy impact DiD, DiD policy assessment
Saistītās	4	4
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.	Policy Evaluation DiD applies the difference-in-differences estimator specifically to assess the causal impact of government programs, regulations, or policy reforms. It compares outcome changes in a group exposed to the policy against a comparable untreated group, before and after the policy took effect, isolating the net policy effect from pre-existing trends and time-common shocks.