Clustering and causality inference using algorithmic complexity

We present a set of algorithmic complexity estimates. We derive a normalized semi-distance that is shown to outperform the state-of-the-art. We also propose estimators for causality inference on directed acyclic graphs. Illustrative applications include clustering of human writing systems and causality assessment on novel drafts.