Causal Inference Challenges and New Directions for Epidemiologic Research on the Health Effects of Social Policies

Abstract

Epidemiologic research on the health effects of social policies is growing rapidly because of the potentially large impact of these policies on population health and health equity. We describe key methodological challenges faced in this nascent field and promising tools to enhance the validity of future studies. In epidemiologic studies of social policies, causal identification is most commonly pursued through confounder-control but use of instrument-based approaches is increasing. Researchers face challenges measuring relevant policy exposures; addressing confounding and positivity violations arising from co-occurring policies and time-varying confounders; deriving precise effect estimates; and quantifying and accounting for interference. Promising tools to address these challenges can enhance both internal validity (randomization, front door criterion for causal identification, new estimators that address interference and practical positivity violations) and external validity (data-driven methods for evaluating heterogeneous treatment effects; methods for transporting and generalizing effect estimates to new populations). Common threats to validity in epidemiologic research play out in distinctive ways in research on the health effects of social policies. This is an active area of methodologic development, with ongoing advances to support causal inferences and produce policy-relevant findings. Researchers must navigate the tension between research questions of greatest interest and research questions that can be answered most accurately and precisely with the data at hand. Additional work is needed to facilitate integration of modern epidemiologic methods with econometric tools for policy evaluation and to increase the size and measurement quality of datasets.