Ellen C Caniglia1, Eleanor J Murray2. 1. Department of Population Health, New York University Langone Medical Center. 2. Department of Epidemiology, Boston University School of Public Health.
Abstract
PURPOSE OF REVIEW: The goal of this article is to provide an introduction to the intuition behind the difference-in-difference method for epidemiologists. We focus on the theoretical aspects of this tool, including the types of questions for which difference-in-difference is appropriate, and what assumptions must hold for the results to be causally interpretable. RECENT FINDINGS: While currently under-utilized in epidemiologic research, the difference-in-difference method is a useful tool to examine effects of population-level exposures, but relies on strong assumptions. SUMMARY: We use the famous example of John Snow's investigation of the cause of cholera mortality in London to illustrate the difference-in-difference approach and corresponding assumptions. We conclude by arguing that this method deserves a second-look from epidemiologists interested in asking causal questions about the impact of a population-level exposure change on a population-level outcome for the group that experienced the change.
PURPOSE OF REVIEW: The goal of this article is to provide an introduction to the intuition behind the difference-in-difference method for epidemiologists. We focus on the theoretical aspects of this tool, including the types of questions for which difference-in-difference is appropriate, and what assumptions must hold for the results to be causally interpretable. RECENT FINDINGS: While currently under-utilized in epidemiologic research, the difference-in-difference method is a useful tool to examine effects of population-level exposures, but relies on strong assumptions. SUMMARY: We use the famous example of John Snow's investigation of the cause of cholera mortality in London to illustrate the difference-in-difference approach and corresponding assumptions. We conclude by arguing that this method deserves a second-look from epidemiologists interested in asking causal questions about the impact of a population-level exposure change on a population-level outcome for the group that experienced the change.
Entities:
Keywords:
John Snow; causal inference; change scores; difference in difference
Authors: Alvaro Castillo-Carniglia; William R Ponicki; Andrew Gaidus; Paul J Gruenewald; Brandon D L Marshall; David S Fink; Silvia S Martins; Ariadne Rivera-Aguirre; Garen J Wintemute; Magdalena Cerdá Journal: Epidemiology Date: 2019-03 Impact factor: 4.822
Authors: Rebecca M Lebeaux; Juliette C Madan; Quang P Nguyen; Modupe O Coker; Erika F Dade; Yuka Moroishi; Thomas J Palys; Benjamin D Ross; Melinda M Pettigrew; Hilary G Morrison; Margaret R Karagas; Anne G Hoen Journal: Pediatr Res Date: 2022-05-14 Impact factor: 3.953