English

A generalized back-door criterion

Methodology 2015-06-04 v3 Artificial Intelligence

Abstract

We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variables that satisfies our generalized back-door criterion, when considering a single intervention and a single outcome variable. Moreover, if such a set exists, we provide an explicit set that fulfills the criterion. We illustrate the results in several examples. R-code is available in the R-package pcalg.

Keywords

Cite

@article{arxiv.1307.5636,
  title  = {A generalized back-door criterion},
  author = {Marloes H. Maathuis and Diego Colombo},
  journal= {arXiv preprint arXiv:1307.5636},
  year   = {2015}
}

Comments

Published at http://dx.doi.org/10.1214/14-AOS1295 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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