An Improved Indicator for Causal Interaction in Non-Linear Systems

Utilizing an extension of Pearson’s correlation in the case of random vectors, we improve the empirical dynamic modeling causal analysis of non-linear systems. To prove the effectiveness of the use of such an extension we analyze two real-world examples, the paramecium-didinium protozoan system and the influence of environmental variables on mosquito abundance in northern Greece. In both examples it is shown that the causal analysis based on the extended metric outperforms the usual method of measuring the correlation between observed and predicted values of a single vector component.