Compare methods

	Synthetic Control for Health Policy	Interrupted Time Series for Public Health
Field	Social Epidemiology	Social Epidemiology
Family	Process / pipeline	Process / pipeline
Year of origin	2010	2002
Originator	Alberto Abadie & Javier Gardeazabal; Alberto Abadie, Alexis Diamond & Jens Hainmueller	Anita K. Wagner, Stephen B. Soumerai et al. (segmented-regression formulation); James Lopez Bernal, Steven Cummins & Antonio Gasparrini (public-health tutorial)
Type	Comparative case-study design constructing a weighted comparator for an aggregate health-policy unit	Quasi-experimental design estimating level and slope changes in a population outcome after an intervention
Seminal source	Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93(1), 113-132.	Wagner, A. K., Soumerai, S. B., Zhang, F., & Ross-Degnan, D. (2002). Segmented Regression Analysis of Interrupted Time Series Studies in Medication Use Research. Journal of Clinical Pharmacy and Therapeutics, 27(4), 299-309.
Aliases	Synthetic Control Health Policy Evaluation, Donor-Pool Comparator for Health Policy, Synthetic Control for Population Health, Weighted Comparator Policy Evaluation	ITS, Segmented Regression Analysis, Interrupted Time Series Analysis, Quasi-Experimental Time Series Evaluation
Related	3	3
Summary	The synthetic control method evaluates the effect of a population-health policy implemented in a single aggregate unit — a state, country, or region — by building a data-driven comparator from a pool of untreated units. When a policy such as a tobacco tax, an alcohol-pricing law, a smoking ban, or a health-insurance expansion is enacted in one place, no single other place is a perfect counterfactual. The method instead forms a synthetic version of the treated unit as a weighted average of donor units chosen so that the synthetic closely tracks the treated unit's outcome and predictors before the policy. The post-intervention gap between the real unit and its synthetic twin estimates the policy's effect. Introduced by Abadie and Gardeazabal and formalized by Abadie, Diamond and Hainmueller — whose canonical application is California's Proposition 99 tobacco-control program — it has become a leading design for evaluating health policies at the population level, with placebo tests providing inference.	Interrupted time series analysis, usually implemented as segmented regression, is a strong quasi-experimental design for evaluating the effect of a public-health intervention introduced at a known point in time. By tracking a population-level outcome — prescribing rates, infections, injuries, hospital admissions — over many equally spaced periods before and after the intervention, it asks whether the outcome's level jumped and whether its underlying trend changed when the intervention took effect, relative to the pre-intervention trajectory projected forward as the counterfactual. The segmented-regression formulation was popularized for intervention research by Wagner, Soumerai and colleagues, and Lopez Bernal, Cummins and Gasparrini's 2017 International Journal of Epidemiology tutorial is the standard modern guide for public-health applications, covering autocorrelation, seasonality, and the use of comparison series.