Diagramas causais: a epidemiologia brasileira de volta para o futuro

Abstract

The search for causal links lies at the core of epidemiology as a scientific field oriented to the study of health-related events in human populations. However, the challenge of causal inference has intrigued philosophers and scientists alike for centuries.Mainly beginning in the late 18 th century, various referential contributions emerged for causal studies in epidemiology. These include the work by Lind and Snow on scurvy and cholera, respectively, the Henle-Koch postulates, within the germ theory, Hill's criteria of causation, Rothman's sufficient-component cause model, Rubin's potential outcomes model, and the vast writings by Miettinen, Robins, and Greenland and their school since the 1970s.The approaches have differed substantially, but one common thread connects all the contributions closest to contemporary epidemiology: the assumption that the possibility of causal inference requires adherence to the principles of validity and precision and the existence of theoretical-operational models that sustain the causal hypotheses in question.However, the good practice of drawing explicit causal models or graphs has not spread as widely as necessary, perhaps because these models tend to be highly difficult to operationalize, given the complexity involved in the determination of the health-disease process at the population level. We also appear to lack a better strategy for applying these models to study design and analysis of epidemiological data.Among Brazilian epidemiology's relevant contributions to causality issues and causal inference, two have sought to lend greater cognizance and practicality to causal models. Almeida Filho 1 has painstakingly tackled the epidemiologist's daunting task of transposing concepts from the theoretical to the empirical level. Meanwhile, Victora et al. 2 emphasizes the role of these conceptual models in the analysis of epidemiological data, introducing the notion of modeling with hierarchical organization of variables.Despite its relevant background and scientific output in causal inference, Brazilian epidemiology appears to have missed the huge methodological advances in this area, in both graduate training and research practice itself. At the international level, causal diagrams and new modeling strategies in causal inference in observational studies have been an area of prolific research at least since the 1980s, with a strong presence in graduate courses and the leading epidemiology journals. For example, in 1986 Robins 3 presented a graphical approach to the identification and estimation of causal parameters in occupational cohort studies potentially subject to the healthy worker bias. Later, a method known as G-estimation was developed to control bias in epidemiological studies when a time-dependent risk factor acts simultaneously as a confounding factor and intermediate variable 4 . The 1990s witnessed the first formal theoretical and conceptual elaboration of causal diagrams, particularly "Directed Acyclic Graphs" (DAGs), thereby unveiling an area of methodological devel...

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