We discuss some problems encountered in inference of directionality of coupling, or, in the case of two interacting systems, in inference of causality from bivariate time series. We identify factors and influences that can lead to either decreased test sensitivity or false detections and propose ways to cope with them in order to perform tests with high sensitivity and a low rate of false positive results.

Institute of Computer Science Academy of Sciences of the Czech Republic Pod vodárenskou vezí 2 182 07 Prague 8 Czech Republic

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The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks

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