Causal influence in linear Langevin networks without feedback

The intuition of causation is so fundamental that almost every research study in life sciences refers to thisconcept. However, a widely accepted formal definition of causal influence between observables is still missing.In the framework of linear Langevin networks without feedback (linear response models) we propose a measureof causal influence based on a new decomposition of information flows over time. We discuss its main propertiesand we compare it with other information measures like the transfer entropy. We are currently unable to extendthe definition of causal influence to systems with a general feedback structure and nonlinearities