Causal diagrams for epidemiologic research.
摘要：
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.
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DOI：
10.1097/00001648-199901000-00008
被引量：
年份：
1999
Taylor & Francis
国家科技图书文献中心
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BMJ
万方医学
掌桥科研
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