Cause-Effect Preservation and Classification using Neurochaos Learning

TL;DR

This paper demonstrates that Neurochaos Learning effectively classifies cause-effect relationships in various simulated and real-world systems, outperforming deep neural networks and preserving causality through chaotic transformations.

Contribution

It introduces Neurochaos Learning as a novel approach for cause-effect classification and causality preservation in chaotic and real-world data.

Findings

NL outperforms deep neural networks in cause-effect classification.

NL preserves causality in feature space using GC and CCC.

NL successfully classifies cause-effect in chaotic and real-world systems.

Abstract

Discovering cause-effect from observational data is an important but challenging problem in science and engineering. In this work, a recently proposed brain inspired learning algorithm namely-\emph{Neurochaos Learning} (NL) is used for the classification of cause-effect from simulated data. The data instances used are generated from coupled AR processes, coupled 1D chaotic skew tent maps, coupled 1D chaotic logistic maps and a real-world prey-predator system. The proposed method consistently outperforms a five layer Deep Neural Network architecture for coupling coefficient values ranging from $0.1$ to $0.7$. Further, we investigate the preservation of causality in the feature extracted space of NL using Granger Causality (GC) for coupled AR processes and and Compression-Complexity Causality (CCC) for coupled chaotic systems and real-world prey-predator dataset. This ability of NL to preserve causality under a chaotic transformation and successfully classify cause and effect time series (including a transfer learning scenario) is highly desirable in causal machine learning applications.