Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Causal networks: semantics and expressiveness
UAI '88 Proceedings of the Fourth Annual Conference on Uncertainty in Artificial Intelligence
Learning Bayesian Networks
Minimal separators in dependency structures: Properties and identification
Cybernetics and Systems Analysis
An algorithm for finding minimum d-separating sets in belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Rules for efficiently finding minimal d-separating sets in DAG dependency models are inferred based on the d-separation criterion and acyclic property of a digraph. The rules allow one to acceleratedly identify the presence or absence of edges in a model. These rules use only unconditional independencies and conditional independencies of the first rank. Versions of the causal faithfulness assumption are formulated that justify the use of these rules in inferring the skeleton of a model from statistical data.