Ab initio algorithmic causal deconvolution of intertwined programs and networks by generative mechanism

​Complex data is usually produced by interacting sources with different mechanisms. Here we introduce a parameter-free model-based approach, based upon the seminal concept of Algorithmic Probability, that decomposes an observation and signal into its most likely algorithmic generative sources. Our methods use a causal calculus to infer model representations. We demonstrate the method ability to distinguish interacting mechanisms and deconvolve them, regardless of whether the objects produce strings, space-time evolution diagrams, images or networks. We numerically test and evaluate our causal separation methods and find that it can disentangle examples of observations from discrete dynamical systems, and complex networks. We think that these causal separating techniques can contribute to tackle the challenge of causation for estimations of better rooted probability distributions thereby complementing more limited statistical-oriented techniques that otherwise would lack model inference capabilities.

Ab initio algorithmic causal deconvolution of intertwined programs and networks by generative mechanism.pdf