×

zbMATH — the first resource for mathematics

Causal diagrams for empirical research (with discussion). (English) Zbl 0860.62045
The tools introduced in this paper are aimed at helping researchers communicate qualitative assumptions about cause-effect relationships, elucidate the ramifications of such assumptions, and derive causal inferences from a combination of assumptions, experiments, and data.
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.

MSC:
62G99 Nonparametric inference
62-09 Graphical methods in statistics (MSC2010)
05C90 Applications of graph theory
62A01 Foundations and philosophical topics in statistics
62P99 Applications of statistics
PDF BibTeX XML Cite
Full Text: DOI