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0 results for “causal discovery”
High-Order Markov Blanket Discovery via a k-Order Relaxation of the Faithfulness Assumption
A new research paper introduces a k-order relaxation of the faithfulness assumption to improve Markov blanket discovery in graphical models, addressing known failure modes from higher-order dependencies and finite-sample artifacts.
Jul 30, 2026
DKCD: Domain Knowledge-Enhanced Causal Discovery from Unstructured Data
A new research framework called DKCD improves causal discovery from unstructured data in high-expertise domains by integrating domain knowledge into LLM-based reasoning, addressing latent factor identification and annotation reliability.
Jul 13, 2026
How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confounding
A new arXiv preprint identifies and characterizes two distinct failure regimes of Bayesian causal discovery methods when applied to linear Gaussian models with additive latent confounding between exactly two observed variables, showing that increasing sample size lowers the correlation threshold at which spurious edges are favoured.
Jul 13, 2026