Rigorous policy measurement: causal inference challenges and opportunities

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Abstract
Epidemiologists are increasingly asking questions about the effects of policies on health and health disparities, generally using quasi-experimental methods. Researchers have developed a burgeoning body of rigorous methodological work focused on addressing potential inference challenges arising from modeling choices, study design, data availability, and common sources of bias in policy evaluations using observational data. However, epidemiologists have paid less attention to the measurement and operationalization of policy exposures. The field of legal epidemiology offers rigorous, formalized methods to address challenges in measuring policy, yet disciplinary divides have impeded the communication of these approaches from lawyers to epidemiologists. In this article, we use terminology familiar to epidemiologists to describe the field of legal epidemiology and how challenges in measuring policy exposures can compromise causal inference, with a particular focus on addressing information bias and consistency assumptions. Laws and regulations can address or enforce structural inequities, and understanding challenges to their characterization and measurement can enhance epidemiologic research on their health and health equity effects. This article is part of a Special Collection on Methods in Social Epidemiology.
Funding Information
  • Drexel FIRST (Faculty Institutional Recruitment for Sustainable Transformation) Program, National Institutes of Health (U54CA267735-03)
  • National Institutes of Allergy and Infectious Diseases (K01AI168579)

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