Quasiprobability models in Bayesian networks generalize to produce all non-signalling correlations for a broad class of networks and conjecturally recover the nested Markov model.
Foundations of structural causal models with cycles and latent variables
4 Pith papers cite this work. Polarity classification is still indexing.
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Introduces the order-theoretic property of conicality for space-times and proves a correspondence between conical space-times and faithful information-theoretic causal models under no-superluminal-signalling constraints.
Modifies Gibbs sampler for GP state-space models, introduces CFA measurement structure, and validates software via simulation-based calibration to enable reliable learning of nonlinear latent dynamics.
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Characterizing Signalling: Connections between Causal Inference and Space-time Geometry
Introduces the order-theoretic property of conicality for space-times and proves a correspondence between conical space-times and faithful information-theoretic causal models under no-superluminal-signalling constraints.