A cut-point-invariant circular Chatterjee correlation is defined via averaging in circular rank space; it equals zero under independence and one under measurable functional dependence for non-atomic circular marginals.
On the power of chatterjee’s rank correlation
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Causality-encoded diffusion models use a known DAG to train graph-consistent conditional diffusions for observational recovery, interventional sampling via fixed-variable propagation, and a resampling-based directed edge test with convergence rates depending on local dimension.
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A Circular Chatterjee's Correlation Coefficient
A cut-point-invariant circular Chatterjee correlation is defined via averaging in circular rank space; it equals zero under independence and one under measurable functional dependence for non-atomic circular marginals.
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Causality-Encoded Diffusion Models for Interventional Sampling and Edge Inference
Causality-encoded diffusion models use a known DAG to train graph-consistent conditional diffusions for observational recovery, interventional sampling via fixed-variable propagation, and a resampling-based directed edge test with convergence rates depending on local dimension.