arXiv Machine Learning August 12, 2026 By Pavel Averin, Theodoros Moysiadis, Ioannis Katakis

Conditional Independence Tests for Constraint-Based Causal Discovery: A Survey

Read the original on arXiv Machine Learning →

arXiv:2608. 11156v1 Announce Type: cross Abstract: Conditional Independence (CI) tests are the statistical engine of constraint-based causal discovery: in algorithms such as PC (Peter-Clark) and FCI (Fast Causal Inference), skeleton pruning and key orientations follow directly from CI decisions.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

efficiency

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safety

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Related stories

Fast Nonparametric Conditional Independence Testing via Two-Stage Regression

Toward Scalable and Valid Conditional Independence Testing with Spectral Representations

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