Granger Causality from Quantized Measurements

TL;DR

This paper introduces a novel method for inferring Granger causality from quantized Gaussian signals, utilizing a rank-based criterion and matrix analysis to reliably detect causality without system parameter identification.

Contribution

It provides a new rank criterion and conditions for inferring Granger causality from quantized data, including binary and high-resolution quantization, without requiring system model parameters.

Findings

The causality matrix's smallest singular value bounds distribution differences.

Conditions for reliable causality inference from quantized measurements are established.

Simulation results demonstrate the method's effectiveness.

Abstract

An approach is proposed for inferring Granger causality between jointly stationary, Gaussian signals from quantized data. First, a necessary and sufficient rank criterion for the equality of two conditional Gaussian distributions is proved. Assuming a partial finite-order Markov property, a characterization of Granger causality in terms of the rank of a matrix involving the covariances is presented. We call this the causality matrix. The smallest singular value of the causality matrix gives a lower bound on the distance between the two conditional Gaussian distributions appearing in the definition of Granger causality and yields a new measure of causality. Then, conditions are derived under which Granger causality between jointly Gaussian processes can be reliably inferred from the second order moments of quantized measurements. A necessary and sufficient condition is proposed for Granger causality inference under binary quantization. Furthermore, sufficient conditions are introduced to infer Granger causality between jointly Gaussian signals through measurements quantized via non-uniform, uniform or high resolution quantizers. Apart from the assumed partial Markov order and joint Gaussianity, this approach does not require the parameters of a system model to be identified. No assumptions are made on the identifiability of the jointly Gaussian random processes through the quantized observations. The effectiveness of the proposed method is illustrated by simulation results.