# Learning Generalized Causal Structure in Time-series

arXiv: https://arxiv.org/abs/2112.03085 · HTML: https://mlsanity.com/paper/2112.03085/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Causal Inference for Time series Analysis: Problems, Methods and Evaluation](https://mlsanity.com/paper/2102.05829/) (2021)
- [The State of Applied Econometrics - Causality and Policy Evaluation](https://mlsanity.com/paper/1607.00699/) (2016)
- [ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification](https://mlsanity.com/paper/1910.02423/) (2019)
- [When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning](https://mlsanity.com/paper/2102.01316/) (2021)
- [A Neurochaos Learning Architecture for Genome Classification](https://mlsanity.com/paper/2010.10995/) (2020)

## Abstract (arXiv)

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.