Learning Generalized Causal Structure in Time-series

TL;DR

This paper introduces a machine learning pipeline that leverages neurochaos-based feature extraction to learn generalized causal structures in time-series data, addressing the limitations of traditional ML in understanding temporal causality.

Contribution

It presents a novel approach combining neurochaos feature learning with causal structure discovery in time-series, enhancing the ability to understand temporal causality.

Findings

Successfully learns causal structures in time-series data

Addresses limitations of traditional ML in temporal causality

Demonstrates improved causal inference capabilities

Abstract

The science of causality explains/determines 'cause-effect' relationship between the entities of a system by providing mathematical tools for the purpose. In spite of all the success and widespread applications of machine-learning (ML) algorithms, these algorithms are based on statistical learning alone. Currently, they are nowhere close to 'human-like' intelligence as they fail to answer and learn based on the important "Why?" questions. Hence, researchers are attempting to integrate ML with the science of causality. Among the many causal learning issues encountered by ML, one is that these algorithms are dumb to the temporal order or structure in data. In this work we develop a machine learning pipeline based on a recently proposed 'neurochaos' feature learning technique (ChaosFEX feature extractor), that helps us to learn generalized causal-structure in given time-series data.