### Abstract

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

Title of host publication | Proceedings of the Twenty-First Conference Conference on Uncertainty in Artificial Intelligence (2005) |

Publisher | AUAI Press |

Pages | 10-17 |

Number of pages | 8 |

ISBN (Print) | 974903914 |

Publication status | Published - 1 Jan 2005 |

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### Cite this

*Proceedings of the Twenty-First Conference Conference on Uncertainty in Artificial Intelligence (2005)*(pp. 10-17). AUAI Press.

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*Proceedings of the Twenty-First Conference Conference on Uncertainty in Artificial Intelligence (2005).*AUAI Press, pp. 10-17.

**Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables.** / ALI, Ayesha; RICHARDSON, Thomas; SPIRTES, Peter; ZHANG, Jiji.

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

TY - GEN

T1 - Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables

AU - ALI, Ayesha

AU - RICHARDSON, Thomas

AU - SPIRTES, Peter

AU - ZHANG, Jiji

PY - 2005/1/1

Y1 - 2005/1/1

N2 - It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one representation. In this paper, we address what is less well known: how do the relationships common to every causal explanation among the observed variables of some DAG process change in the presence of latent variables? Ancestral graphs provide a class of graphs that can encode conditional independence relations that arise in DAG models with latent and selection variables. In this paper we present a set of orientation rules that construct the Markov equivalence class representative for ancestral graphs, given a member of the equivalence class. These rules are sound and complete. We also show that when the equivalence class includes a DAG, the equivalence class representative is the essential graph for the said DAG

AB - It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one representation. In this paper, we address what is less well known: how do the relationships common to every causal explanation among the observed variables of some DAG process change in the presence of latent variables? Ancestral graphs provide a class of graphs that can encode conditional independence relations that arise in DAG models with latent and selection variables. In this paper we present a set of orientation rules that construct the Markov equivalence class representative for ancestral graphs, given a member of the equivalence class. These rules are sound and complete. We also show that when the equivalence class includes a DAG, the equivalence class representative is the essential graph for the said DAG

UR - https://dslpitt.org/uai/papers/05/p10-ali.pdf

UR - http://commons.ln.edu.hk/sw_master/4386

M3 - Conference paper (refereed)

SN - 974903914

SP - 10

EP - 17

BT - Proceedings of the Twenty-First Conference Conference on Uncertainty in Artificial Intelligence (2005)

PB - AUAI Press

ER -