Causality in epidemiology

Epidemiology represents an interesting and unique example of cross-fertilization between social and natural sciences. Epidemiology has evolved from a monocausal to a multicausal concept of the "web of causation", thus mimicking a similar and much earlier shift in the social sciences. However, in comparison with the social sciences epidemiology is both more sensitive to underlying biological models (which condition the interpretation of population findings), and more prone to a simplification of the causal pathways. Paradoxically, epidemiology has developed more sophisticated theoretical models for bias and confounding than the social sciences did, but for the practical purpose of identifying single preventable risk factors. Epidemiology makes use more often of study designs that simulate experimentation, than of surveys in the general population.