title: [Causal analysis approaches in epidemiology].
abstract: Epidemiological research is mostly based on observational studies. Whether such studies can provide evidence of causation remains discussed. Several causal analysis methods have been developed in epidemiology. This paper aims at presenting an overview of these methods: graphical models, path analysis and its extensions, and models based on the counterfactual approach, with a special emphasis on marginal structural models. Graphical approaches have been developed to allow synthetic representations of supposed causal relationships in a given problem. They serve as qualitative support in the study of causal relationships. The sufficient-component cause model has been developed to deal with the issue of multicausality raised by the emergence of chronic multifactorial diseases. Directed acyclic graphs are mostly used as a visual tool to identify possible confounding sources in a study. Structural equations models, the main extension of path analysis, combine a system of equations and a path diagram, representing a set of possible causal relationships. They allow quantifying direct and indirect effects in a general model in which several relationships can be tested simultaneously. Dynamic path analysis further takes into account the role of time. The counterfactual approach defines causality by comparing the observed event and the counterfactual event (the event that would have been observed if, contrary to the fact, the subject had received a different exposure than the one he actually received). This theoretical approach has shown limits of traditional methods to address some causality questions. In particular, in longitudinal studies, when there is time-varying confounding, classical methods (regressions) may be biased. Marginal structural models have been developed to address this issue. In conclusion, "causal models", though they were developed partly independently, are based on equivalent logical foundations. A crucial step in the application of these models is the formulation of causal hypotheses, which will be a basis for all methodological choices. Beyond this step, statistical analysis tools recently developed offer new possibilities to delineate complex relationships, in particular in life course epidemiology.
authors.affiliation: Inserm U1018, équipe épidémiologie respiratoire et environnementale, CESP centre de recherche en épidémiologie et santé des populations, 16, avenue Paul-Vaillant-Couturier, 94807 Villejuif, France; UMRS 1018, université Paris Sud 11, 94807 Villejuif, France. Electronic address: orianne.dumas@inserm.fr.
authors.forename: O
authors.initials: O
authors.lastname: Dumas
authors.affiliation: Inserm U823, centre de recherche Albert-Bonniot, 38042 La Tronche, France; Université Joseph-Fourier, 38041 Grenoble, France.
authors.forename: V
authors.initials: V
authors.lastname: Siroux
authors.affiliation: Inserm U1018, équipe épidémiologie respiratoire et environnementale, CESP centre de recherche en épidémiologie et santé des populations, 16, avenue Paul-Vaillant-Couturier, 94807 Villejuif, France; UMRS 1018, université Paris Sud 11, 94807 Villejuif, France.
authors.forename: N
authors.initials: N
authors.lastname: Le Moual
authors.affiliation: Inserm U1018, équipe épidémiologie respiratoire et environnementale, CESP centre de recherche en épidémiologie et santé des populations, 16, avenue Paul-Vaillant-Couturier, 94807 Villejuif, France; UMRS 1018, université Paris Sud 11, 94807 Villejuif, France.
authors.forename: R
authors.initials: R
authors.lastname: Varraso
mesh_terms: D000465:Algorithms; D015984:Causality; D016021:Epidemiologic Studies; D006801:Humans; D008962:Models, Theoretical; D012107:Research Design
keywords: Biais (Épidémiologie); Bias (Epidemiology); Causality; Causalité; Confounding factors (Epidemiology); Facteur de confusion (Épidémiologie); Models; Modèle structural; Structural