Caniglia Ellen C, Murray Eleanor J
Department of Population Health, New York University Langone Medical Center.
Department of Epidemiology, Boston University School of Public Health.
Curr Epidemiol Rep. 2020 Dec;7(4):203-211. doi: 10.1007/s40471-020-00245-2. Epub 2020 Sep 23.
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.
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.
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.
本文旨在向流行病学家介绍差分法背后的直观原理。我们聚焦于该工具的理论层面，包括适用于差分法的问题类型，以及要使结果具有因果解释力必须满足哪些假设。
虽然差分法目前在流行病学研究中的应用尚不充分，但它是检验人群水平暴露效应的有用工具，不过依赖于强有力的假设。
我们用约翰·斯诺对伦敦霍乱死亡率原因的著名调查案例来说明差分法及其相应假设。我们得出结论，对于那些有兴趣就人群水平暴露变化对经历该变化的群体的人群水平结果的影响提出因果问题的流行病学家而言，这种方法值得重新审视。
