  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.
