title: Causal inference in public health.
abstract: 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.
authors.affiliation: Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, USA. tglass@jhsph.edu
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Glass
authors.forename: Steven N
authors.initials: SN
authors.lastname: Goodman
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Hernán
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Samet
mesh_terms: D015984:Causality; D006293:Health Promotion; D006801:Humans; D017063:Outcome Assessment, Health Care; D011634:Public Health / Q000706:statistics & numerical data*