Cause-Effect Preservation and Classification using Neurochaos Learning

0556947 - ÚI 2023 US eng V - Research Report
Harikrishnan, N. B. - Kathpalia, Aditi - Nagaraj, N.
Cause-Effect Preservation and Classification using Neurochaos Learning.
Cornell University, 2022. 13 s. arXiv.org e-Print archive, arXiv:2201.12181.
R&D Projects: GA ČR(CZ) GA19-16066S
Grant - others:AV ČR(CZ) AP1901
Program: Akademická prémie - Praemium Academiae
Institutional support: RVO:67985807
https://arxiv.org/abs/2201.12181v1

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.
Permanent Link: http://hdl.handle.net/11104/0331065