Hypersurfaces and their singularities in partial correlation testing

Abstract

An asymptotic theory is developed for computing volumes of regions in the parameter space of a directed Gaussian graphical model that are obtained by bounding partial correlations. We study these volumes using the method of real log canonical thresholds from algebraic geometry. Our analysis involves the computation of the singular loci of correlation hypersurfaces. Statistical applications include the strong-faithfulness assumption for the PC-algorithm, and the quantification of confounder bias in causal inference. A detailed analysis is presented for trees, bow-ties, tripartite graphs, and complete graphs.

References in corpus (2)

• Estimating high-dimensional directed acyclic graphs with the PC-algorithm
• Geometry of the faithfulness assumption in causal inference

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