  A complete set of practical estimators for the conditional, simple and joint
algorihmic complexities is presented, from which a semi-metric is derived.
Also, new directed information estimators are proposed that are applied to
causality inference on Directed Acyclic Graphs. The performances of these
estimators are investigated and shown to compare well with respect to the
state-of-the-art Normalized Compression Distance (NCD).
