  'Causal' direction is of great importance when dealing with complex systems.
Often big volumes of data in the form of time series are available and it is
important to develop methods that can inform about possible causal connections
between the different observables. Here we investigate the ability of the
Transfer Entropy measure to identify causal relations embedded in emergent
coherent correlations. We do this by firstly applying Transfer Entropy to an
amended Ising model. In addition we use a simple Random Transition model to
test the reliability of Transfer Entropy as a measure of `causal' direction in
the presence of stochastic fluctuations. In particular we systematically study
the effect of the finite size of data sets.
