Much of epidemiologic research is aimed at uncovering the causes of disease and identifying potential risk or protective factors. The demonstration of a statistical association between a disease and a potential risk factor does not necessarily imply a causal relationship; chance, confounding or other forms of bias have to be carefully discussed as further explanations for any observed statistical association.
Even if chance and bias can be ruled out, further careful evaluation is warranted. A well-known and widely used framework for deriving causal inference has been proposed by Hill (1965) and provides a helpful orientation for causal inference (see Table 13.2). However, it should not be considered as a checklist as it does not allow clearly distinguishing causal from noncausal relations. For a more in-depth discussion of the Hill criteria and some encountered problems, we refer to some standard textbooks (e.g., Rothman and Greenland, 1998; Rothman, 2002).
TABLE 13.2 Causal criteria proposed by Hill (1965)
• Strength of the association
• Consistency of the association
• Specificity of the association
• Temporal sequence of events
• Dose-response relationship, biologic gradient
• Coherence with existent theories and knowledge
• Experimental evidence
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