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.
Annual Review of Statistics and Its Application , volume=
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A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.
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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.
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Structure Learning for Directed Trees with Zero-Inflated Compositional Nodes
A new directed tree structure learning framework for zero-inflated compositional nodes uses KL divergence scoring and column-stochastic transition matrices for conditional expectations, with proven consistency and finite-sample guarantees.