• causality
• theoretical methods

In his recent article describing what characterises a useful concept of causation in epidemiology,¹ Olsen provides a useful overview of the now popular component-cause model and its relevance for epidemiological research, and renews the call for discussion on how best to conceptualise causation. As he rightly points out, this is not merely of academic interest—how we view causation influences (whether consciously or not) the way in which we frame research questions and analyse and interpret epidemiological data. In recent decades, the component-cause model has been the predominant causal framework on which epidemiological research has been based, and it has been of great use for the identification of individual risk factors associated with disease and the development of the advanced statistical techniques that are now widely used for this purpose. Based on these successes, Olsen argues for the usefulness of the component-cause model over more recently propounded frameworks based on a probabilistic view of causality.^2,3 In doing so, however, we feel he is rather hasty in accepting a deterministic future for epidemiology.

The great value of the component-cause model lies in its heuristic power. A person, through exposure to various risk factors, eventually accumulates a combination of contributing exposures that constitute a “sufficient cause” and that, under identical conditions, invariably lead to disease. As visualised by Rothman and Greenland,⁴ these contributing exposures, or “component causes”, form the slices of a “causal pie” that, when complete, constitute a “sufficient cause”. This deterministic model provides a useful framework with which to conceptualise causation in a chronological manner, from first exposure to a component cause all the way to the completion of the “causal pie” and subsequent disease. It is here, however, that the component-cause model faces its greatest problem. Epidemiology is …