Introduces a Bayesian order-based learning method for multiple DAGs that uses heterogeneity to enhance causal ordering identifiability up to two permutations, with a new R2R proposal for efficient posterior sampling in high dimensions.
Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
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Leveraging heterogeneity for identifiability: Bayesian order-based learning of multiple DAGs
Introduces a Bayesian order-based learning method for multiple DAGs that uses heterogeneity to enhance causal ordering identifiability up to two permutations, with a new R2R proposal for efficient posterior sampling in high dimensions.