Complex causal process diagrams for analyzing the health impacts of policy interventions

Am J Public Health. 2006 Mar;96(3):473-9. doi: 10.2105/AJPH.2005.063693. Epub 2006 Jan 31.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Audiovisual Aids / statistics & numerical data*
  • Data Interpretation, Statistical
  • Health Policy*
  • Humans
  • Models, Statistical*
  • Public Health Practice / statistics & numerical data*
  • Systems Theory*