Estimating causal effects from epidemiological data
Estimating causal effects from epidemiological data
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DOI：
10.1136/jech.2004.029496
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发表时间：
2006-07-01
影响因子：
6.3
通讯作者：
Robins, JM
中科院分区：
文献类型：
--
作者：
Hernán, MA;Robins, JM
在理想的随机实验中，关联就是因果关系：关联度量可以被解释为效果度量，因为随机化确保暴露的和未暴露的是可交换的。另一方面，在观察性研究中，关联通常不是因果关系：关联度量不能被解释为效果度量，因为暴露的和未暴露的通常不能互换。然而，观察性研究往往是因果推理的唯一选择。本文回顾了允许从观测数据中估计因果效应的条件，以及在该条件下估计总体因果效应的两种方法--标准化和逆概率加权。为简单起见，主要描述限于二分变量，并假设不存在可归因于采样变异性的随机误差。附录提供了逆概率加权的一般形式。
In ideal randomised experiments, association is causation: association measures can be interpreted as effect measures because randomisation ensures that the exposed and the unexposed are exchangeable. On the other hand, in observational studies, association is not generally causation: association measures cannot be interpreted as effect measures because the exposed and the unexposed are not generally exchangeable. However, observational research is often the only alternative for causal inference. This article reviews a condition that permits the estimation of causal effects from observational data, and two methods - standardisation and inverse probability weighting - to estimate population causal effects under that condition. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a generalisation of inverse probability weighting.