title: [Causality and confounding in epidemiology].
abstract: In theory, a cause of an effect in an individual and a group can be defined. However, in empirical studies the requirements of this definition cannot be fulfilled with certainty: an individual or a group of people cannot be exposed and unexposed at the same point in time. Therefore, substitute populations are used to answer what the risk of an outcome would have been, if the actually exposed group would not have been exposed (or vice versa). If the substitute population is not able to deliver this information, confounding is present according to the counterfactual definition. The so-called collapsibility definition of confounders suffers from five limitations and therefore does not appear to be acceptable. The classical theory of confounders is a special case of directed acyclic graphs (DAGs), where only one extraneous variable might be a potential confounder. In contrast to previous theories on confounding, DAGs are able to show when adjustment for covariates produces bias. Furthermore, DAGs are able to use also information on relations among confounders.
authors.affiliation: Institut für Klinische Epidemiologie, Medizinische Fakultät, Martin-Luther-Universität Halle-Wittenberg, Halle (Saale). andreas.stang@medizin.uni-halle.de
authors.forename: A
authors.initials: A
authors.lastname: Stang
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D015984:Causality; D003196:Computer Graphics; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods