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.

翻译：因果关系科学通过提供数学工具来解释/ 确定系统实体之间“ 因果” 的关系。 尽管机器学习算法的成功和广泛应用, 这些算法仅以统计学学习为基础。 目前, 它们与“ 为何” 问题无法回答和学习, 离“ 像人一样” 的智能还很遥远。 因此, 研究人员试图将 ML 与因果关系科学结合起来。 在ML 遇到的许多因果学习问题中, 一个问题是这些算法对数据的时间顺序或结构是愚蠢的。 在这项工作中, 我们根据最近提出的“ neurochaos” 特征学习技术开发了一个机器学习管道( CHausFEX 特征提取器), 帮助我们在特定的时间序列数据中学习普遍的因果结构 。