  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.
