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MCMC Structure Learning for Causal DAGs under Interval-Valued Uncertainty: A Bayesian Procedure with Asymptotic Correctness

https://doi.org/10.2139/ssrn.7199406
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Credal networks
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Causality
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Probabilistic machine learning and artificial intelligence
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Partition MCMC for Inference on Acyclic Digraphs
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Joint structure learning and causal effect estimation for categorical graphical models
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Bayesian Model Selection of Gaussian Directed Acyclic Graph Structures
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Discovering Causal Structures in Bayesian Gaussian Directed Acyclic Graph Models
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Causal Structure Learning
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Structure Learning in Graphical Modeling
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Learning Causal Bayesian Network Structures From Experimental Data
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Robustification of the PC-Algorithm for Directed Acyclic Graphs
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Learning high-dimensional directed acyclic graphs with latent and selection variables
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Addendum on the scoring of Gaussian directed acyclic graphical models
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Bayesian Structure Learning in Sparse Gaussian Graphical Models
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Learning Markov Equivalence Classes of Directed Acyclic Graphs: An Objective Bayes Approach
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Bayesian Causal Inference in Probit Graphical Models
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Bayesian Optimal Experimental Design for Inferring Causal Structure
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Bayesian Sample Size Determination for Causal Discovery
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A characterization of Markov equivalence classes for acyclic digraphs
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Parameter priors for directed acyclic graphical models and the characterization of several probability distributions
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A characterization of the Dirichlet distribution through global and local parameter independence
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Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG
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Bayesian model selection consistency for high-dimensional discrete graphical models
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BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
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