arXiv Machine Learning June 5, 2026 By Alek Fr\"ohlich, Vladimir R. Kostic, Karim Lounici, Daniel Perazzo, Daniel Tiezzi, Massimiliano Pontil

Toward Scalable and Valid Conditional Independence Testing with Spectral Representations

Read the original on arXiv Machine Learning →

arXiv:2512. 19510v2 Announce Type: replace Abstract: Conditional independence (CI) is central to causal inference, feature selection, and graphical modeling, yet it is untestable in many settings without additional assumptions.

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arXiv Machine Learning

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arXiv:2504. 12594v2 Announce Type: replace Abstract: Conditional independence testing is a critical component of feature screening, invariant statistical models, and causal discovery.

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arXiv Machine Learning

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Sequential Kernel-based Conditional Independence Testing via Adaptive Betting

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