Causal Diagrams for Empirical Research

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 suucient for identifying causal eeects from non-experimental data. If so the diagrams can be queried to produce mathematical expressions for causal eeects 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.

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