Causation Inference and Information Flow in Dynamical Systems: Theory and Applications
Inferring causal interactions and relationships is a fundamental task pursued by scientists across many disciplines. While questions of causation are traditionally answered by testing hypotheses through carefully controlled, real experiments, recent computational approaches attempt to detect causal relations using purely observational data. Articles in this Focus Issue discuss both the theory of such approaches and their applications to problems across multiple scientific disciplines.
Guest Editors: Erik M. Bollt, Jie Sun and Jakob Runge