Causation and causal inference in epidemiology
Causation and causal inference in epidemiology
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DOI：
10.2105/ajph.2004.059204
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发表时间：
2005-01-01
影响因子：
12.7
通讯作者：
Greenland, S
中科院分区：
文献类型：
--
作者：
Rothman, KJ;Greenland, S
因果和因果推理的概念在很大程度上是从早期的学习经验中自学的。一个用充分原因及其组成原因来描述原因的因果模型阐明了多声学、组成原因的强度依赖于互补的组成原因的流行以及组成原因之间的相互作用等重要原则。哲学家们一致认为因果命题是不能证明的，并在所有因果推理哲学中发现了缺陷或实践局限性。因此，逻辑、信念和观察在评估因果命题中的作用尚未确定。流行病学中的因果推断更好地被视为一种测量效果的练习，而不是决定是否存在一种效果的标准指导过程。
Concepts of cause and causal inference are largely self-taught from early learning experiences. A model of causation that describes causes in terms of sufficient causes and their component causes illuminates important principles such as multicausality, the dependence of the strength of component causes on the prevalence of complementary component causes, and interaction between component causes.Philosophers agree that causal propositions cannot be proved, and find flaws or practical limitations in all philosophies of causal inference. Hence, the role of logic, belief, and observation in evaluating causal propositions is not settled. Causal inference in epidemiology is better viewed as an exercise in measurement of an effect rather than as a criterion-guided process for deciding whether an effect is present or not.