Practical Kernel Tests of Conditional Independence

• URL: http://arxiv.org/abs/2402.13196v1
• Date: Tue, 20 Feb 2024 18:07:59 GMT
• Title: Practical Kernel Tests of Conditional Independence
• Authors: Roman Pogodin, Antonin Schrab, Yazhe Li, Danica J. Sutherland, Arthur Gretton
• Abstract summary: A major challenge of conditional independence testing is to obtain the correct test level while still attaining competitive test power. We propose three methods for bias control to correct the test level, based on data splitting, auxiliary data, and (where possible) simpler function classes.
• Score: 34.7957227546996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We describe a data-efficient, kernel-based approach to statistical testing of conditional independence. A major challenge of conditional independence testing, absent in tests of unconditional independence, 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 obtained using nonparametric kernel ridge regression. We propose three methods for bias control to correct the test level, based on data splitting, auxiliary data, and (where possible) simpler function classes. We show these combined strategies are effective both for synthetic and real-world data.

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