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.
Keywords: John Snow; causal inference; change scores; difference in difference.