DeepAI AI Chat
Log In Sign Up

Inferring Causal Networks of Dynamical Systems through Transient Dynamics and Perturbation

06/23/2020
by   George Stepaniants, et al.
MIT
University of Washington
0

Inferring causal relations from time series measurements is an ill-posed mathematical problem, where typically an infinite number of potential solutions can reproduce the given data. We explore in depth a strategy to disambiguate between possible underlying causal networks by perturbing the network, where the actuations are either targeted or applied at random. The resulting transient dynamics provide the critical information necessary to infer causality. Two methods are shown to provide accurate causal reconstructions: Granger causality (GC) with perturbations, and our proposed perturbation cascade inference (PCI). Perturbed GC is capable of inferring smaller networks under low coupling strength regimes. Our proposed PCI method demonstrated consistently strong performance in inferring causal relations for small (2-5 node) and large (10-20 node) networks, with both linear and nonlinear dynamics. Thus the ability to apply a large and diverse set of perturbations/actuations to the network is critical for successfully and accurately determining causal relations and disambiguating between various viable networks.

READ FULL TEXT
01/19/2021

Interpretable Models for Granger Causality Using Self-explaining Neural Networks

Exploratory analysis of time series data can yield a better understandin...
09/02/2019

Inferring species interactions using Granger causality and convergent cross mapping

Identifying directed interactions between species from time series of th...
01/31/2023

Recurrences reveal shared causal drivers of complex time series

Many experimental time series measurements share an unobserved causal dr...
02/11/2019

Reconstructing dynamical networks via feature ranking

Empirical data on real complex systems are becoming increasingly availab...
11/22/2019

Economy Statistical Recurrent Units For Inferring Nonlinear Granger Causality

Granger causality is a widely-used criterion for analyzing interactions ...