Causal attribution fractions, and the attribution of smoking and BMI to the landscape of disease incidence in UK Biobank.

ABSTRACT: Unlike conventional epidemiological studies that use observational data to estimate "associations" between risk factors and disease, the science of causal inference has identified situations where causal estimates can be made from observational data, using results such as the "backdoor criteria". Here these results are combined with established epidemiological methods, to calculate simple population attribution fractions that estimate the causal influence of risk factors on disease incidence, and can be estimated using conventional proportional hazards methods. A counterfactual argument gives an attribution fraction for individuals. Causally meaningful attribution fractions cannot be constructed for all risk factors or confounders, but they can for the important established risk factors of

SUBMITTER: Webster AJ 

PROVIDER: S-EPMC9667855 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature