The augmented synthetic control method in public health and biomedical research

Abstract

Estimating treatment (or policy or intervention) effects on a single individual or unit has become increasingly important in health and biomedical sciences. One method to estimate these effects is the synthetic control method, which constructs a synthetic control, a weighted average of control units that best matches the treated unit’s pre-treatment outcomes and other relevant covariates. The intervention’s impact is then estimated by comparing the post-intervention outcomes of the treated unit and its synthetic control, which serves as a proxy for the counterfactual outcome had the treated unit not experienced the intervention. The augmented synthetic control method, a recent adaptation of the synthetic control method, relaxes some of the synthetic control method’s assumptions for broader applicability. While synthetic controls have been used in a variety of fields, their use in public health and biomedical research is more recent, and newer methods such as the augmented synthetic control method are underutilized. This paper briefly describes the synthetic control method and its application, explains the augmented synthetic control method and its differences from the synthetic control method, and estimates the effects of an antimalarial initiative in Mozambique using both the synthetic control method and the augmented synthetic control method to highlight the advantages of using the augmented synthetic control method to analyze the impact of interventions implemented in a single region.