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Biometrika 1995 82(4):669-688; doi:10.1093/biomet/82.4.669
© 1995 by Biometrika Trust
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Articles

Causal diagrams for empirical research

JUDEA PEARL

Cognitive Systems Laboratory, Computer Science Department, University of California Los Angeles, California 90024, U.S.A.

Received for publication 1 May 1994. Accepted for publication 1 February 1995.


  Abstract

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.

Key Words: Causal inference • Graph model • Structural equations • Treatment effect


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