Estimating causal effects
Estimating causal effects
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DOI：
10.1093/ije/31.2.422
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发表时间：
2002-04-01
影响因子：
7.7
通讯作者：
Greenland, S
中科院分区：
文献类型：
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作者：
Maldonado, G;Greenland, S
虽然病原流行病学的一个目标是估计暴露对疾病发生的“真实影响”，但流行病学家通常不会精确地说明他们想要估计的“真实影响”是什么。我们描述了最初在哲学和统计学中发展起来的反事实因果理论如何适用于流行病学研究，从而为“什么是原因？”，“我们应该如何衡量效果？”和“流行病学家在病原学研究中应该估计什么样的效应测量？”“我们还表明，反事实理论(1)为设计和分析病因学研究提供了一个总体框架；(2)表明我们在估计效应时必须始终依赖于替换步骤，因此我们估计的有效性将始终依赖于替换的有效性；(3)导致对效果测度、混淆、混杂和效果测度修正的精确定义；并且(4)说明了当因果因素的分布在人群中发生变化时，为什么效应测量应该在人群中发生变化。
Although one goal of aetiologic epidemiology is to estimate ‘the true effect’of an exposure on disease occurrence, epidemiologists usually do not precisely specify what ‘true effect’they want to estimate. We describe how the counterfactual theory of causation, originally developed in philosophy and statistics, can be adapted to epidemiological studies to provide precise answers to the questions ‘What is a cause?’,‘How should we measure effects?’and ‘What effect measure should epidemiologists estimate in aetiologic studies?’We also show that the theory of counterfactuals (1) provides a general framework for designing and analysing aetiologic studies;(2) shows that we must always depend on a substitution step when estimating effects, and therefore the validity of our estimate will always depend on the validity of the substitution;(3) leads to precise definitions of effect measure, confounding, confounder, and effect-measure modification; and (4) shows why effect measures should be expected to vary across populations whenever the distribution of causal factors varies across the populations.