Nonlinear Causal Discovery for High Dimensional Data: A Kernelized Trace Method

Abstract

Causal discovery for high-dimensional observations is a useful tool in many fields such as climate analysis and financial market analysis. A linear Trace method has been proposed to identify the causal direction between two linearly coupled high-dimensional observations X and Y. However, in reality, the relations between X and Y are usually nonlinear and… (More)
DOI: 10.1109/ICDM.2013.103

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