Granger Causality Maps for Langevin Systems

TL;DR

This paper introduces an analytical method to compute Granger causality maps for Langevin systems, overcoming previous computational limitations and enabling analysis even in unstable regions of phase space.

Contribution

It derives a closed-form expression for GC rates in VOU processes, improving computational efficiency and handling unstable dynamics, thus enhancing phase space causality mapping.

Findings

Provides an analytical formula for GC rates in VOU processes.

Enables GC map computation in unstable phase space regions.

Shows invariance of GC rates under fluctuation rescaling.

Abstract

Wahl et al. (2016, 2017) introduced the idea of Granger causality (GC) maps for Langevin systems: dynamics are localised linearly at each point in phase space as vector Ornstein-Uhlenbeck (VOU) processes, for which GCs may in principle be calculated, thus constructing a GC map on phase space. Their implementation, however, suffered a significant drawback: GCs were approximated from models based on discrete-time stroboscopic sampling of local VOU processes, which is not only computationally inefficient, but more seriously, unfeasible on regions of phase space where local dynamics are unstable, leaving "holes" in the GC maps. We solve these problems by deriving an analytical expression for GC rates associated with a VOU process which, under quite general conditions, yields a meaningful solution even in the unstable case. Applied to GC maps, this not only "fills in the holes", but also furnishes a computationally efficient method of calculation devolving to solution of continuous-time algebraic Riccati equations which, in the case of a univariate source, become simple quadratic equations. We show, furthermore, that the GC rate for VOU processes is invariant under rescaling of the overall fluctuations intensity, so that GC maps may effectively be calculated for deterministic nonlinear dynamical systems, with a residual "ghost of noise" represented by a variance-covariance map.