An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
Kiani, Narsis A.
KAUST DepartmentBiological and Environmental Sciences and Engineering (BESE) Division
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Preprint Posting Date2017-09-07
Online Publication Date2019-08-08
Permanent link to this recordhttp://hdl.handle.net/10754/625448
MetadataShow full item record
AbstractWe introduce a conceptual framework and an interventional calculus to steer and manipulate systems based on their intrinsic algorithmic probability using the universal principles of the theory of computability and algorithmic information. By applying sequences of controlled interventions to systems and networks, we estimate how changes in their algorithmic information content are reflected in positive/negative shifts towards and away from randomness. The strong connection between approximations to algorithmic complexity (the size of the shortest generating mechanism) and causality induces a sequence of perturbations ranking the network elements by the steering capabilities that each of them is capable of. This new dimension unmasks a separation between causal and non-causal components providing a suite of powerful parameter-free algorithms of wide applicability ranging from optimal dimension reduction, maximal randomness analysis and system control. We introduce methods for reprogramming systems that do not require the full knowledge or access to the system's actual kinetic equations or any probability distributions. A causal interventional analysis of synthetic and regulatory biological networks reveals how the algorithmic reprogramming qualitatively reshapes the system's dynamic landscape. For example, during cellular differentiation we find a decrease in the number of elements corresponding to a transition away from randomness and a combination of the system's intrinsic properties and its intrinsic capabilities to be algorithmically reprogrammed can reconstruct an epigenetic landscape. The interventional calculus is broadly applicable to predictive causal inference of systems such as networks and of relevance to a variety of machine and causal learning techniques driving model-based approaches to better understanding and manipulate complex systems.
CitationZenil H, Kiani NA, Marabita F, Deng Y, Elias S, et al. (2017) An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems. Available: http://dx.doi.org/10.1101/185637.
SponsorsWe want to thank the reviewers for their valuable input. Author Contributions H.Z., N.A.K., and J.T. conceived and designed the methods. H.Z. and N.A.K. are responsible for data acquisition. H.Z., N.A.K., Y.D., F.M. contributed to data analysis. H.Z. and N.A.K. developed the methodology, with key contributions from J.T. H.Z. performed most of the numerical experiments with Y.D. and F.M. also contributing. H.Z., A.S., G.B., and S.E. contributed the literature-based Th17 enrichment analysis. H.Z. and J.T. wrote the article with key contributions from N.A.K. H.Z. was supported by the Swedish Research Council grant no. 2015-05299. Declaration of Interests The authors declare no competing interests.
RelationsIs Supplemented By:
Except where otherwise noted, this item's license is described as This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).