Inferring causality from noisy time series data

TL;DR

This paper evaluates Convergent Cross-Mapping (CCM) for causal inference in noisy time series, revealing its limitations and proposing noise injection as a potential solution to improve accuracy.

Contribution

The study systematically assesses CCM's performance under noise and coupling variations, and introduces controlled noise injection to enhance causal inference accuracy.

Findings

CCM struggles with synchronized and intermediate coupling data.

Noise reduces cross-mapping fidelity but convergence rate is robust.

Controlled noise injections can improve causal inference in coupled systems.

Abstract

Convergent Cross-Mapping (CCM) has shown high potential to perform causal inference in the absence of models. We assess the strengths and weaknesses of the method by varying coupling strength and noise levels in coupled logistic maps. We find that CCM fails to infer accurate coupling strength and even causality direction in synchronized time-series and in the presence of intermediate coupling. We find that the presence of noise deterministically reduces the level of cross-mapping fidelity, while the convergence rate exhibits higher levels of robustness. Finally, we propose that controlled noise injections in intermediate-to-strongly coupled systems could enable more accurate causal inferences. Given the inherent noisy nature of real-world systems, our findings enable a more accurate evaluation of CCM applicability and advance suggestions on how to overcome its weaknesses.