RT Journal Article
SR Electronic
T1 An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems
JF bioRxiv
FD Cold Spring Harbor Laboratory Press
DO 10.1101/185637
A1 Zenil, Hector
A1 Kiani, Narsis A.
A1 Marabita, Francesco
A1 Deng, Yue
A1 Elias, Szabolcs
A1 Schmidt, Angelika
A1 Ball, Gordon
A1 Tegner, Jesper
YR 2017
UL http://biorxiv.org/content/early/2017/09/07/185637.1.abstract
AB We 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.