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Most machine learning approaches extract statistical features from data, rather than the underlying causal mechanisms. A different approach analyses information in a general way by extracting recursive patterns from data using generative models under the paradigm of computability and algorithmic information theory.
Complex behaviour emerges from interactions between objects produced by different generating mechanisms. Yet to decode their causal origin(s) from observations remains one of the most fundamental challenges in science. This paper introduces a universal, unsupervised and parameter-free model-oriented approach, based on the seminal concept and the first principles of algorithmic probability, to decompose an observation into its most likely algorithmic generative models.
