Direct and indirect causal effects via potential outcomes

被引:196
|
作者
Rubin, DB [1 ]
机构
[1] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA
关键词
anthrax vaccine; biomarkers; causal inference; principal stratification; Rubin Causal Model; surrogate outcomes;
D O I
10.1111/j.1467-9469.2004.02-123.x
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
The use of the concept of 'direct' versus 'indirect' causal effects is common, not only in statistics but also in many areas of social and economic sciences. The related terms of 'biomarkers' and 'surrogates' are common in pharmacological and biomedical sciences. Sometimes this concept is represented by graphical displays of various kinds. The view here is that there is a great deal of imprecise discussion surrounding this topic and, moreover, that the most straightforward way to clarify the situation is by using potential outcomes to define causal effects. In particular, I suggest that the use of principal stratification is key to understanding the meaning of direct and indirect causal effects. A current study of anthrax vaccine will be used to illustrate ideas.
引用
收藏
页码:161 / 170
页数:10
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