Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Bayesian network learning algorithms using structural restrictions
International Journal of Approximate Reasoning
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Strong completeness and faithfulness in Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Critical remarks on single link search in learning belief networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Detecting marginal and conditional independencies between events and learning their causal structure
ECSQARU'13 Proceedings of the 12th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
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This paper presents an extension to the Conservative PC algorithm which is able to detect violations of adjacency faithfulness under causal sufficiency and triangle faithfulness. Violations can be characterized by pseudo-independent relations and equivalent edges, both generating a pattern of conditional independencies that cannot be modeled faithfully. Both cases lead to uncertainty about specific parts of the skeleton of the causal graph. These ambiguities are modeled by an f-pattern. We prove that our Adjacency Conservative PC algorithm is able to correctly learn the f-pattern. We argue that the solution also applies for the finite sample case if we accept that only strong edges can be identified. Experiments based on simulations and the ALARM benchmark model show that the rate of false edge removals is significantly reduced, at the expense of uncertainty on the skeleton and a higher sensitivity for accidental correlations.