Causal Diagrams for Empirical Research
展示了如何用图形模型作为数学语言整合统计与领域知识，为非实验数据下的因果效应识别提供非参数框架，并指导如何通过额外观测或实验获得所需推断。
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.