摘要：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. CAUSAL DIAGRAMS ARE a useful way of summarizing information not only for presentation and communication but also for analysis. They can specify causal relationships for modeling in a way that is different from traditional epidemiology, in which “modeling” tends to be used in the sense of statistical modeling (an inductive approach). There is potential for more use both of diagrammatic ways of organizing causal relationships in complex systems and of a priori modeling that specifies causal pathways, an approach that is well established in other disciplines, such as air pollution modeling and management studies. 1 , 2
