  Wiener-Granger causality is a widely used framework of causal analysis for
temporally resolved events. We introduce a new measure of Wiener-Granger
causality based on kernelization of partial canonical correlation analysis with
specific advantages in the context of large high-dimensional data. The
introduced measure is able to detect non-linear and non-monotonous signals, is
designed to be immune to noise, and offers tunability in terms of computational
complexity in its estimations. Furthermore, we show that, under specified
conditions, the introduced measure can be regarded as an estimate of
conditional mutual information (transfer entropy). The functionality of this
measure is assessed using comparative simulations where it outperforms other
existing methods. The paper is concluded with an application to climatological
data.
