Partial information decomposition components map directly onto causal roles such as direct parents, children, and colliders in both pairwise Bayesian networks and higher-order hypergraphs.
Causality: Models, Reasoning and Inference
2 Pith papers cite this work. Polarity classification is still indexing.
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The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.
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Information-theoretic signatures of causality in Bayesian networks and hypergraphs
Partial information decomposition components map directly onto causal roles such as direct parents, children, and colliders in both pairwise Bayesian networks and higher-order hypergraphs.
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Causal Discovery via Quantile Partial Effect
The paper demonstrates that assuming the quantile partial effect lies in a finite linear span enables causal identifiability from observational data, with applications to bivariate and multivariate causal discovery using basis tests and Fisher information.