Speaker
Description
Inference of causal structure between multiple observations of a complex system gained large interest in wide range of scientific disciplines, from pharmaceutics to economy, where it earned Nobel-prize for Clive Granger in 2003. In this talk, we present a new analysis method called Dimensional Causality (DC). We belive, our method is the first one, which is able to detect and distinguish all possible types of causal relations: independence, directed or circular causal connection and particularly the existence of a hidden common cause. To detect these relations between two time series, a dynamical system’s theoretical approach is combined with the Bayesian model inference. We validated our method on simulated examples of classical chaotic and non-chaotic dynamical systems such as coupled Lorentz-systems, Logistic maps, or Hindmarsh-Rose models and demonstrated its capabilities on human neurophysiological measurements. As an example of medical application, the possible focus of epileptic seizure (an area which drives the others) is identified in a patient, based on implanted electrode recordings from the surface of the brain. However, the universality of our method ensures its applicability in many other fields of science.
