On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias

Research output: Journal PublicationsJournal Article (refereed)

108 Citations (Scopus)

Abstract

Causal discovery becomes especially challenging when the possibility of latent confounding and/or selection bias is not assumed away. For this task, ancestral graph models are particularly useful in that they can represent the presence of latent confounding and selection effect, without explicitly invoking unobserved variables. Based on the machinery of ancestral graphs, there is a provably sound causal discovery algorithm, known as the FCI algorithm, that allows the possibility of latent confounders and selection bias. However, the orientation rules used in the algorithm are not complete. In this paper, we provide additional orientation rules, augmented by which the FCI algorithm is shown to be complete, in the sense that it can, under standard assumptions, discover all aspects of the causal structure that are uniquely determined by facts of probabilistic dependence and independence. The result is useful for developing any causal discovery and reasoning system based on ancestral graph models.
Original languageEnglish
Pages (from-to)1873-1896
Number of pages24
JournalArtificial Intelligence
Volume172
Issue number16–17
DOIs
Publication statusPublished - 1 Nov 2008
Externally publishedYes

Keywords

  • Ancestral graphs
  • Automated causal discovery
  • Bayesian networks
  • Causal models
  • Latent variables
  • Markov equivalence

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