GKCM generalizes kernel CI testing to arbitrary regression models, provides uniform asymptotic level guarantees under stated conditions, and outperforms state-of-the-art methods in simulations when using tree-based regressors.
Pub- lisher: Institute of Mathematical Statistics
2 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
2
Pith papers citing it
6
external citations · OpenAlex
representative citing papers
A spectral generalized covariance measure enables conditional independence testing on non-Euclidean data with uniform bootstrap validity and power guarantees under doubly robust conditions.
citing papers explorer
-
The Generalised Kernel Covariance Measure
GKCM generalizes kernel CI testing to arbitrary regression models, provides uniform asymptotic level guarantees under stated conditions, and outperforms state-of-the-art methods in simulations when using tree-based regressors.