  The intuition of causation is so fundamental that almost every research study
in life sciences refers to this concept. However a widely accepted formal
definition of causal influence between observables is still missing. In the
framework of linear Langevin networks without feedbacks (linear response
models) we developed a measure of causal influence based on a decomposition of
information flows over time. We discuss its main properties and compare it with
other information measures like the Transfer Entropy. Finally we outline some
difficulties of the extension to a general definition of causal influence for
complex systems.
