Confounding and collapsibility in causal inference

被引：10

作者：

Greenland, S ^{[1
]}

Robins, JM

Pearl, J

机构：

[1] Univ Calif Los Angeles, Sch Publ Hlth, Dept Epidemiol, Los Angeles, CA 90095 USA

[2] Harvard Univ, Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA

[3] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA

[4] Univ Calif Los Angeles, Sch Engn & Appl Sci, Dept Comp Sci, Los Angeles, CA 90095 USA

来源：

STATISTICAL SCIENCE | 1999年 / 14卷 / 01期

关键词：

bias; causation; collapsibility; confounding; contingency tables; exchangeability; observational studies; odds ratio; relative risk; risk assessment; Simpson's paradox;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Consideration of confounding is fundamental to the design and analysis of studies of causal effects. Yet, apart from confounding in experimental designs, the topic is given little or no discussion in most statistics texts. We here provide an overview of confounding and related concepts based on a counterfactual model for causation. Special attention is given to definitions of confounding, problems in control of confounding, the relation of confounding to exchangeability and collapsibility, and the importance of distinguishing confounding from noncollapsibility.

引用

页码：29 / 46

页数：18

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