Causal diagrams for empirical research 论文
1995Biometrika引用 2313
Bayesian Modeling and Causal InferenceStatistical Methods and InferenceStatistical Methods and Bayesian Inference
详细信息
- 发表期刊/会议
- Biometrika
- 发表日期
- 1995-01-01
- 发表年份
- 1995
关键词
Bayesian Modeling and Causal InferenceStatistical Methods and InferenceStatistical Methods and Bayesian Inference
摘要
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.