Causal diagrams for empirical research
Bibliographic Data
| ID | 23355145 |
|---|---|
| Authors | Judea Pearl (University of California, Los Angeles, corresponding author) |
| Year | 1995 |
| Volume | 82 |
| Issue | 4 |
| Pages | 669-688 |
| Publication date | 1995-01-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Biometrika (JOURNAL) |
| Journal identifiers | ISSN: 0006-3444 • E-ISSN: 1464-3510 |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1093/biomet/82.4.669 |
| OpenAlex | W2049910836 |
| Language | EN |
| Citations received | 167 |
| References cited | 7 |
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.
Causal inference · Causal model · Data mining · Econometrics · Inference · Nonparametric statistics · Statistical inference · Statistics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Computer Science · Mathematics · Statistical Methods and Bayesian Inference · Statistical Methods and Inference · Theoretical Computer Science
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Correlation and Causation
| Unique citing works | 167 |
|---|---|
| Citations per year | 5,39 |
| Citation span | 1995 - 2026 (32) |
| Citation velocity | current |
| Highly cited | Yes |
| Citation types | Neutral: 157 |