Causal deconvolution by algorithmic generative models

@article{Zenil2019CausalDB,
  title={Causal deconvolution by algorithmic generative models},
  author={Hector Zenil and Narsis Aftab Kiani and Allan A. Zea and Jesper N. Tegner},
  journal={Nature Machine Intelligence},
  year={2019},
  volume={1},
  pages={58-66}
}
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. Here we introduce 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. Our approach uses a… 

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