Conditions for non-confounding and collapsibility without knowledge of completely constructed causal diagrams

被引:7
|
作者
Geng, Z [1 ]
Li, GW [1 ]
机构
[1] Peking Univ, Dept Probabil & Stat, Beijing 100871, Peoples R China
关键词
causal inference; collapsibility; confounding; graphical model;
D O I
10.1111/1467-9469.00087
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
In this paper, we discuss several concepts in causal inference in terms of causal diagrams proposed by Pearl (1993, 1995a, b), and we give conditions for non-confounding, homogeneity and collapsibility for causal effects without knowledge of a completely constructed causal diagram. We first introduce the concepts of non-confounding, conditional non-confounding, uniform non-confounding, homogeneity, collapsibility and strong collapsibility for causal effects, then we present necessary and sufficient conditions for uniform non confounding, homegeneity and collapsibilities, and finally we show sufficient conditions for, non-confounding, conditional non-confounding and uniform non-confounding.
引用
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页码:169 / 181
页数:13
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