Difference-in-Difference in the Time of Cholera: a Gentle Introduction for Epidemiologists
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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.
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Difference-in-differences is a widely used observational causal inference method in health policy for evaluating the real-world effects of policies and programs.
“Difference-in-differences (DiD) is a popular observational causal inference method in health policy, employed to evaluate the real-world impact of policies and programs.”
From Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods (2025) · cites this paper as [1]
The Conclusions describe difference-in-differences as a method for estimating the causal impact of population-level changes and discuss evaluating policies to improve population health. But the Introduction says it is used relatively infrequently by epidemiologists, and the Conclusions locate its widespread use in economics; the paper does not support calling it popular in health policy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredDiD can estimate treatment effects even when no comparison group is exactly comparable to the treatment group.
“DiD allows for treatment effect estimation even when there exist no comparison groups that are exactly comparable to the treatment groups.”
From Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods (2025) · cites this paper as [1]
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References 14 · Cited by 52
- Authors
- Caniglia, E. C., & Murray, E. J.
- Affiliations
- NYU Langone Health (US)
- Boston University (US)
- Corresponding author
- Murray, Eleanor J.
- Venue
- Current Epidemiology Reports
- Published
- 2020-09-23
- Volume
- 7
- Issue
- 4
- Pages
- 203-211
- ISSN
- 2196-2995 (ISSN-L)
- PMC
- PMC8006863
- OpenAlex
- W3088797378
- Cited by
- 52
- References
- 14
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 11 · 2023: 8 · 2022: 8 · 2021: 2 · 2020: 1
- FWCI
- 3.35
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Epidemiology Reports (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (K01HD100222)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Sociology and Political Science › Historical and modern epidemiology studies
- Keywords
- difference-in-differences, causal inference, cholera mortality, John Snow, cholera outbreak investigation, causal assumptions, potential outcomes, epidemiological study design
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