Compare methods

	Synthetic Control for Health Policy	Negative Control Outcome Design
Field	Social Epidemiology	Social Epidemiology
Family	Process / pipeline	Process / pipeline
Year of origin	2010	2010
Originator	Alberto Abadie & Javier Gardeazabal; Alberto Abadie, Alexis Diamond & Jens Hainmueller	Marc Lipsitch, Eric Tchetgen Tchetgen & Ted Cohen; Xu Shi & Wang Miao
Type	Comparative case-study design constructing a weighted comparator for an aggregate health-policy unit	Falsification-and-correction pipeline for unmeasured confounding
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.	Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010). Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies. Epidemiology, 21(3), 383-388.
Aliases	Synthetic Control Health Policy Evaluation, Donor-Pool Comparator for Health Policy, Synthetic Control for Population Health, Weighted Comparator Policy Evaluation	Negative Controls, Negative Control Outcome, Negative Control Exposure, Falsification Endpoint Analysis
Related	3	4
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.	The negative control design uses a deliberately chosen outcome (or exposure) that cannot plausibly be caused by the exposure under study, yet is subject to the same unmeasured confounding, selection, or measurement processes as the real research question. If the exposure appears to 'affect' something it cannot possibly affect, that spurious association is a signature of residual bias. Lipsitch, Tchetgen Tchetgen, and Cohen formalized this falsification logic for epidemiology in 2010, specifying the conditions a valid negative control must satisfy. Shi, Miao, and Tchetgen Tchetgen's 2020 review extended the idea from detection toward correction, showing how pairs of negative control variables underpin proximal causal inference, which can recover an unbiased effect estimate even when the confounder is never measured.