Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
A transformational characterization of equivalent Bayesian network structures
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Ancestral graphs provide a class of graphs that can encode conditional independence relations that arise in directed acyclic graph (DAG) models with latent and selection variables, corresponding to marginalization and conditioning. However, for any ancestral graph, there may be several other graphs to which it is Markov equivalent. We introduce a simple representation of a Markov equivalence class of ancestral graphs, thereby facilitating the model search process for some given data. More specifically, we define a join operation on ancestral graphs which will associate a unique graph with an equivalence class. We also extend the separation criterion for ancestral graphs (which is an extension of d-separation) and provide a proof of the pairwise Markov property for joined ancestral graphs. Proving the pairwise Markov property is the first step towards developing a global Markov property for these graphs. The ultimate goal of this work is to obtain a full characterization of the structure of Markov equivalence classes for maximal ancestral graphs, thereby extending analogous results for DAGs given by Frydenberg (1990), Verma and Pearl (1991), Chickering (1995) and Andersson et al. (1997).