Causal diagrams for empirical research
Causal diagrams for empirical research
复制标题
DOI：
10.2307/2337329
复制
发表时间：
1995-12-01
期刊：
影响因子：
2.7
通讯作者：
Pearl, J
中科院分区：
文献类型：
--
作者：
Pearl, J
本文的主要目的是展示如何图形模型可以作为一种数学语言，整合统计和主题信息。特别是，本文开发了一个原则性的，非参数框架的因果推理，在图中查询，以确定是否有足够的假设，从非实验数据识别因果关系的影响。如果是这样，则可以查询图以产生根据观察到的分布的因果效应的数学表达式;否则，可以查询图以建议可以从中获得所需推断的额外观察或辅助实验。
The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper develops a principled, nonparametric framework for causal inference, in which diagrams are queried to determine if the assumptions available are sufficient for identifying causal effects from nonexperimental data. If so the diagrams can be queried to produce mathematical expressions for causal effects in terms of observed distributions; otherwise, the diagrams can be queried to suggest additional observations or auxiliary experiments from which the desired inferences can be obtained.