Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links

TL;DR

This paper presents a machine learning method using reservoir computing to infer short-term causal links in dynamical systems from time series data, emphasizing the effects of noise on inference accuracy.

Contribution

The paper introduces a novel reservoir computing approach for causal inference in dynamical systems, estimating Jacobian matrices from short-term predictions.

Findings

Dynamical noise enhances inference accuracy.

Observational noise degrades inference performance.

The noise balance influences causal inference success.

Abstract

We introduce and test a general machine-learning-based technique for the inference of short term causal dependence between state variables of an unknown dynamical system from time series measurements of its state variables. Our technique leverages the results of a machine learning process for short time prediction to achieve our goal. The basic idea is to use the machine learning to estimate the elements of the Jacobian matrix of the dynamical flow along an orbit. The type of machine learning that we employ is reservoir computing. We present numerical tests on link inference of a network of interacting dynamical nodes. It is seen that dynamical noise can greatly enhance the effectiveness of our technique, while observational noise degrades the effectiveness. We believe that the competition between these two opposing types of noise will be the key factor determining the success of causal inference in many of the most important application situations.