Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, USA.
Annu Rev Public Health. 2013;34:61-75. doi: 10.1146/annurev-publhealth-031811-124606. Epub 2013 Jan 7.
Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action's consequences rather than the less precise notion of a risk factor's causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world.
因果推断在公共卫生中具有核心作用；确定关联是因果关系表明了干预的可能性。我们回顾并评论了长期以来用于解释证据支持因果关系的指南，并将其与鼓励从干预角度思考原因的潜在结果框架进行对比。我们认为，在公共卫生领域，这个框架更为适用，它提供了对行动后果的估计，而不是风险因素因果效应的不那么精确的概念。各种现代统计方法都采用了这种方法。当无法指定干预措施时，因果关系仍然存在，但如何干预以改变结果将不清楚。在应用中，需要承认因果过程的复杂结构，并收集适当的数据来研究它们。这些新方法需要应用于我们全球化世界中日益复杂的公共卫生挑战。
