Practical Kernel Tests of Conditional Independence

• URL: http://arxiv.org/abs/2402.13196v2
• Date: Fri, 19 Sep 2025 19:11:44 GMT
• Title: Practical Kernel Tests of Conditional Independence
• Authors: Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton,
• Abstract summary: SplitKCI is an automated method for bias control for the Kernel-based Conditional Independence test based on data splitting.<n>We show that our approach significantly improves test level control for KCI without sacrificing test power.
• Score: 33.275712245547815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing is to obtain the correct test level (the specified upper bound on the rate of false positives), while still attaining competitive test power. Excess false positives arise due to bias in the test statistic, which is in our case obtained using nonparametric kernel ridge regression. We propose SplitKCI, an automated method for bias control for the Kernel-based Conditional Independence (KCI) test based on data splitting. We show that our approach significantly improves test level control for KCI without sacrificing test power, both theoretically and for synthetic and real-world data.

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