Predictability improvement as a tool to detect causality

A causality method based on predictions in reconstructed state spaces is presented and tested in relation to the ability to detect coupling and synchronization of interconnected dynamical systems. The method is demonstrated on a test example of two uni-directionally coupled chaotic systems of Rössler type. The results show that the predictability of the driven system can be improved by incorporating information about past values of the driving system to the prediction process. This predictability improvement reveals the presence and the direction of the coupling and may help to detect the onset of full synchronization and distinguish causality from mere correlation.