Summary
Causal diagrams have a long history of informal use and, more recently, have undergone formal development for applications in expert systems and robotics. We provide an introduction to these developments and their use in epidemiologic research. Causal diagrams can provide a starting point for identifying variables that must be measured and controlled to obtain unconfounded effect estimates. They also provide a method for critical evaluation of traditional epidemiologic criteria for confounding. In particular, they reveal certain heretofore unnoticed shortcomings of those criteria when used in considering multiple potential confounders. We show how to modify the traditional criteria to correct those shortcomings. (Epidemiology 1999;10:37–48)
Outcomes reported
Referenced by PLOS supermarket placement trial as citation 39; likely supports topic area: supermarket placement / food retail environment. Topics: supermarket placement / food retail environment Evidence type: Research article / other Source report: PLOS supermarket placement trial Ref#: PLOS supermarket placement trial #39 Original: Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48. https://doi.org/10.1097/00001648-199901000-00008 PMID: 9888278
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