Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data

TL;DR

This paper introduces Jacobian Granger Causality (JGC), a neural network method that uses Jacobian measures to identify causal relationships in both stationary and nonstationary time series, outperforming existing methods.

Contribution

The paper presents a novel neural network-based approach for Granger causality that effectively captures causal relationships and temporal dependencies, even in nonstationary systems.

Findings

JGC accurately identifies causal variables and time lags.

The method performs well in both stationary and nonstationary data.

It can learn changing causal structures over time.

Abstract

Granger causality is a commonly used method for uncovering information flow and dependencies in a time series. Here we introduce JGC (Jacobian Granger Causality), a neural network-based approach to Granger causality using the Jacobian as a measure of variable importance, and propose a thresholding procedure for inferring Granger causal variables using this measure. The resulting approach performs consistently well compared to other approaches in identifying Granger causal variables, the associated time lags, as well as interaction signs. Lastly, through the inclusion of a time variable, we show that this approach is able to learn the temporal dependencies for nonstationary systems whose Granger causal structures change in time.