Title

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

Document Type

Book chapter

Source Publication

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

Publication Date

1-1-2005

First Page

10

Last Page

17

Publisher

AUAI Press

Abstract

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

Publisher Statement

Copyright © UAI 2005, AUAI Press.

Access to external full text or publisher's version may require subscription.

Additional Information

ISBN of the source publication: 0974903914

Full-text Version

Publisher’s Version

Recommended Citation

Ali, A., Richardson, T., Spirtes, P., & Zhang, J. (2015). Towards characterizing Markov equivalence classes for directed acyclic graphs with latent variables. In F. Bacchus & T. Jaakkola (Eds.), Proceedings of the Twenty-First Conference Conference on Uncertainty in Artificial Intelligence (2005) (pp.10-17). Arlington, Virginia: AUAI Press. Retrieved from https://dslpitt.org/uai/papers/05/p10-ali.pdf