Causal deconvolution by algorithmic generative models

Hector Zenil, Narsis A. Kiani, Allan A. Zea, Jesper Tegner

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Complex data usually results from the interaction of objects produced by different generating mechanisms. Here we introduce a universal, unsupervised and parameter-free model-oriented approach, based upon the seminal concept of algorithmic probability, that decomposes an observation into its most likely algorithmic generative sources. Our approach uses a causal calculus to infer model representations. We demonstrate its ability to deconvolve interacting mechanisms regardless of whether the resultant objects are strings, space-time evolution diagrams, images or networks. While this is mostly a conceptual contribution and a novel framework, we provide numerical evidence evaluating the ability of our methods to separate data from observations produced by discrete dynamical systems such as cellular automata and complex networks. We think that these separating techniques can contribute to tackling the challenge of causation, thus complementing other statistically oriented approaches.
Original languageEnglish (US)
Pages (from-to)58-66
Number of pages9
JournalNature Machine Intelligence
Volume1
Issue number1
DOIs
StatePublished - Jan 7 2019

Fingerprint

Dive into the research topics of 'Causal deconvolution by algorithmic generative models'. Together they form a unique fingerprint.

Cite this