Data-based intervention approach for Complexity-Causality measure

Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful, as the underlying model generating the data is often unknown. However, existing model-free/data-driven measures assume separability of cause and effect at t...

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Bibliographic Details

Published in:	PeerJ Computer Science
Main Authors:	Aditi Kathpalia, Nithin Nagaraj
Format:	Article
Language:	English
Published:	PeerJ Inc. 2019-05-01
Subjects:	Causality Causal inference Intervention Compression-complexity Model-based Dynamical complexity
Online Access:	https://peerj.com/articles/cs-196.pdf