Causal diagrams in systems epidemiology

The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties.The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets.Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback."Could one of the problems of modern epidemiology ... be that we have drifted back to a posteriori methods - fitting black box equations to data, rather than working out predictions from mathematical modeling of underlying processes?" Norman E Breslow, 2003 [1]."... narrowness of thinking ... pervades much of modern science and leads to inaccurate assessments and prescriptions in many fields. The narrowness itself stems from a perennial challenge with which every scientist must grapple: many phenomena we'd like to understand are highly complex and have multiple, interacting causes." Paul Epstein, 2011 [2].Causation is very important in epidemiology. Epidemiologists are traditionally cautious in using causal concepts: the basic method of e