  We investigate how efficiently a known underlying sparse causality structure
of a simulated multivariate linear process can be retrieved from the analysis
of time-series of short lengths. Causality is quantified from conditional
transfer entropy and the network is constructed by retaining only the
statistically validated contributions. We compare results from three
methodologies: two commonly used regularization methods, Glasso and ridge, and
a newly introduced technique, LoGo, based on the combination of information
filtering network and graphical modelling. For these three methodologies we
explore the regions of time series lengths and model-parameters where a
significant fraction of true causality links is retrieved. We conclude that,
when time-series are short, with their lengths shorter than the number of
variables, sparse models are better suited to uncover true causality links with
LoGo retrieving the true causality network more accurately than Glasso and
ridge.
