Synthetic Control Methods for the Evaluation of Single-Unit Interventions in Epidemiology


A recent study featuring PISC External Advisory Board Member and International Scholar David Humphreys, PhD, and PISC Postdoctoral Fellow Michelle Degli Esposti, PhD, provides a “comprehensive, non-technical tutorial” on the synthetic control method (SCM). By using a data-driven algorithm, SCM offers more accuracy in predicting the outcomes of population-level interventions. In the tutorial, the team focuses on changes to state legislature. Without SCM, researchers compare legislature among different states to determine its impact; however, this can lead to inaccuracies. SCM uses a data-driven algorithm in order to make accurate predictions. The study seeks to improve accessibility of SCM in public health research in order to “enable more widespread and credible implementation of the method in epidemiologic research.”


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