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Causal KL: Evaluating Causal Discovery
The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model.
BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning
In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty.
Latent Variable Discovery Using Dependency Patterns
The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data.
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