Measuring causality by directional symbolic mutual information approach

We propose a novel measure to assess causality through the comparison of symbolic mutual information between the future of one random quantity and the past of the other. This provides a new perspective different from the conventional conceptions. Based on this point of view, a new causality index is derived that uses the definition of directional symbolic mutual information. This measure presents properties different from the time delayed mutual information since the symbolization captures the dynamic features of the analyzed time series. In addition to characterizing the direction and the amplitude of the information flow, it can also detect coupling delays. This method has the property of robustness, conceptual simplicity, and fast computational speed.