Graphical models for causation, and the identification problem

被引:39
作者
Freedman, DA [1 ]
机构
[1] Univ Calif Berkeley, Berkeley, CA 94720 USA
关键词
causation; linear models; graphical models; identification; invariance under intervention;
D O I
10.1177/0193841X04266432
中图分类号
C [社会科学总论];
学科分类号
03 ; 0303 ;
摘要
This article (which is mainly expository) sets up graphical models for causation, having a bit less than the usual complement of hypothetical counterfactuals. Assuming the invariance of error distributions may be essential for causal inference, but the errors themselves need not be invariant. Graphs can be interpreted using conditional distributions, so that we can better address connections between the mathematical framework and causality in the world. The identification problem is posed in terms of conditionals. As will be seen, causal relationships cannot be inferred from a data set by running regressions unless there is substantial prior knowledge about the mechanisms that generated the data. There are few successful applications of graphical models, mainly because few causal pathways can be excluded on a priori grounds. The invariance conditions themselves remain to be assessed.
引用
收藏
页码:267 / 293
页数:27
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