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Michael Joffe, PhD, MD, MSc(Econ) and Jennifer Mindell, MB, BS, PhD, FFPH

Michael Joffe is with Imperial College, London, England. At the time of writing, Jennifer Mindell was also with Imperial College, London.

Correspondence: Requests for reprints should be sent to Michael Joffe, PhD, MD, MSc(Econ), Dept of Epidemiology and Public Health, Imperial College, St Mary’s Campus, Norfolk Pl, London W2 1PF United Kingdom (e-mail: m.joffe{at}imperial.ac.uk).

Causal diagrams are rigorous tools for controlling confounding. They also can be used to describe complex causal systems, which is done routinely in communicable disease epidemiology. The use of change diagrams has advantages over static diagrams, because change diagrams are more tractable, relate better to interventions, and have clearer interpretations.

Causal diagrams are a useful basis for modeling. They make assumptions explicit, provide a framework for analysis, generate testable predictions, explore the effects of interventions, and identify data gaps. Causal diagrams can be used to integrate different types of information and to facilitate communication both among public health experts and between public health experts and experts in other fields. Causal diagrams allow the use of instrumental variables, which can help control confounding and reverse causation.