### Abstract

Original language | English |
---|---|

Title of host publication | Proceedings of the Twenty-Third Conference Conference on Uncertainty in Artificial Intelligence (2007) |

Publisher | AUAI Press |

Pages | 450-457 |

Number of pages | 8 |

ISBN (Print) | 974903930 |

Publication status | Published - 1 Jan 2007 |

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### Cite this

*Proceedings of the Twenty-Third Conference Conference on Uncertainty in Artificial Intelligence (2007)*(pp. 450-457). AUAI Press.

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*Proceedings of the Twenty-Third Conference Conference on Uncertainty in Artificial Intelligence (2007).*AUAI Press, pp. 450-457.

**A characterization of Markov qquivalence classes for directed acyclic graphs with latent variables.** / ZHANG, Jiji.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed)

TY - GEN

T1 - A characterization of Markov qquivalence classes for directed acyclic graphs with latent variables

AU - ZHANG, Jiji

PY - 2007/1/1

Y1 - 2007/1/1

N2 - Different directed acyclic graphs (DAGs) may be Markov equivalent in the sense that they entail the same conditional indepen- dence relations among the observed variables. Meek (1995) characterizes Markov equiva- lence classes for DAGs (with no latent vari- ables) by presenting a set of orientation rules that can correctly identify all arrow orienta- tions shared by all DAGs in a Markov equiv- alence class, given a member of that class. For DAG models with latent variables, maxi- mal ancestral graphs (MAGs) provide a neat representation that facilitates model search. Earlier work (Ali et al. 2005) has identified a set of orientation rules sufficient to con- struct all arrowheads common to a Markov equivalence class of MAGs. In this paper, we provide extra rules sufficient to construct all common tails as well. We end up with a set of orientation rules sound and complete for identifying commonalities across a Markov equivalence class of MAGs, which is partic- ularly useful for causal inference.

AB - Different directed acyclic graphs (DAGs) may be Markov equivalent in the sense that they entail the same conditional indepen- dence relations among the observed variables. Meek (1995) characterizes Markov equiva- lence classes for DAGs (with no latent vari- ables) by presenting a set of orientation rules that can correctly identify all arrow orienta- tions shared by all DAGs in a Markov equiv- alence class, given a member of that class. For DAG models with latent variables, maxi- mal ancestral graphs (MAGs) provide a neat representation that facilitates model search. Earlier work (Ali et al. 2005) has identified a set of orientation rules sufficient to con- struct all arrowheads common to a Markov equivalence class of MAGs. In this paper, we provide extra rules sufficient to construct all common tails as well. We end up with a set of orientation rules sound and complete for identifying commonalities across a Markov equivalence class of MAGs, which is partic- ularly useful for causal inference.

UR - https://dslpitt.org/uai/papers/07/p450-zhang.pdf

UR - http://commons.ln.edu.hk/sw_master/4381

M3 - Conference paper (refereed)

SN - 974903930

SP - 450

EP - 457

BT - Proceedings of the Twenty-Third Conference Conference on Uncertainty in Artificial Intelligence (2007)

PB - AUAI Press

ER -