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This is a review of aspects of the theory of algorithmic information that maycontribute to a framework for formulating questions related to complex highlyunpredictable systems. We start by contrasting Shannon Entropy andKolmogorov-Chaitin complexity epitomizing the difference between correlationand causation to then move onto surveying classical results from algorithmiccomplexity and algorithmic probability, highlighting their deep connection tothe study of automata frequency distributions. We end showing how long-rangealgorithmic predicting models for economic and biological systems may requireinfinite computation but locally approximated short-range estimations arepossible thereby showing how small data can deliver important insights intoimportant features of complex "Big Data".

Algorithmic Data Analytics, Small Data Matters and Correlation versus Causation