A graphical criterion for the identification of causal effects in linear models

Authors:
Carlos Brito;Judea Pearl
Affiliations:
Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA;Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA
Venue:
Eighteenth national conference on Artificial intelligence
Year:
2002

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Abstract

This paper concerns the assessment of direct causal effects from a combination of: (i) non-experimental data, and (ii) qualitative domain knowledge. Domain knowledge is encoded in the form of a directed acyclic graph (DAG), in which all interactions are assumed linear, and some variables are presumed to be unobserved. The paper establishes a sufficient criterion for the identifiability of all causal effects in such models as well as a procedure for estimating the causal effects from the observed covariance matrix.