Causality Testing: A Data Compression Framework

TL;DR

This paper introduces a novel causality testing framework based on data compression, unifying existing methods and overcoming limitations of traditional approaches, especially in noisy and non-synchronous data.

Contribution

It proposes a generic compression-based causality testing framework and a new Compression-Complexity Causality measure that improves upon existing methods.

Findings

Outperforms Granger Causality and Transfer Entropy in noisy data

Works effectively with non-synchronous measurements

Provides insights into the nature of causal influence

Abstract

Causality testing, the act of determining cause and effect from measurements, is widely used in physics, climatology, neuroscience, econometrics and other disciplines. As a result, a large number of causality testing methods based on various principles have been developed. Causal relationships in complex systems are typically accompanied by entropic exchanges which are encoded in patterns of dynamical measurements. A data compression algorithm which can extract these encoded patterns could be used for inferring these relations. This motivates us to propose, for the first time, a generic causality testing framework based on data compression. The framework unifies existing causality testing methods and enables us to innovate a novel Compression-Complexity Causality measure. This measure is rigorously tested on simulated and real-world time series and is found to overcome the limitations of Granger Causality and Transfer Entropy, especially for noisy and non-synchronous measurements. Additionally, it gives insight on the `kind' of causal influence between input time series by the notions of positive and negative causality.