Causal Diagrams for Empirical Research

Article English OPEN
Pearl, Judea (1994)
  • Publisher: eScholarship, University of California
  • Subject: interventions treatment effect | Causal inference | graph models
    acm: ComputerApplications_COMPUTERSINOTHERSYSTEMS
    arxiv: Computer Science::Databases

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 identifiying causal effects from non-experimental 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 auxillary experiments from which the desired inferences can be obtained.
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