# Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda

arXiv: https://arxiv.org/abs/2501.13763 · HTML: https://mlsanity.com/paper/2501.13763/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 17 of 17)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [Random Features Strengthen Graph Neural Networks](https://mlsanity.com/paper/2002.03155/) (2020)
- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [A Tutorial on Learning With Bayesian Networks](https://mlsanity.com/paper/2002.00269/) (2020)
- [Relating Graph Neural Networks to Structural Causal Models](https://mlsanity.com/paper/2109.04173/) (2021)
- [Causal Deep Learning](https://mlsanity.com/paper/2303.02186/) (2023)
- [CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning](https://mlsanity.com/paper/2201.03647/) (2022)
- [ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification](https://mlsanity.com/paper/1910.02423/) (2019)
- [Mapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning](https://mlsanity.com/paper/2410.22077/) (2024)
- [When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning](https://mlsanity.com/paper/2102.01316/) (2021)
- [Exploring Causal Learning through Graph Neural Networks: An In-depth Review](https://mlsanity.com/paper/2311.14994/) (2023)
- [SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2404.07941/) (2024)
- [Stochastic Aggregation in Graph Neural Networks](https://mlsanity.com/paper/2102.12648/) (2021)
- [CausalLP: Learning causal relations with weighted knowledge graph link prediction](https://mlsanity.com/paper/2405.02327/) (2024)
- [Customized Subgraph Selection and Encoding for Drug-drug Interaction Prediction](https://mlsanity.com/paper/2411.01535/) (2024)
- [Graph Neural Stochastic Differential Equations](https://mlsanity.com/paper/2308.12316/) (2023)

## Abstract (arXiv)

Deep learning implemented via neural networks, has revolutionized machine learning by providing methods for complex tasks such as object detection/classification and prediction. However, architectures based on deep neural networks have started to yield diminishing returns, primarily due to their statistical nature and inability to capture causal structure in the training data. Another issue with deep learning is its high energy consumption, which is not that desirable from a sustainability perspective. Therefore, alternative approaches are being considered to address these issues, both of which are inspired by the functioning of the human brain. One approach is causal learning, which takes into account causality among the items in the dataset on which the neural network is trained. It is expected that this will help minimize the spurious correlations that are prevalent in the learned representations of deep neural networks. The other approach is Neurochaos Learning, a recent development, which draws its inspiration from the nonlinear chaotic firing intrinsic to neurons in biological neural networks (brain/central nervous system). Both approaches have shown improved results over just deep learning alone. To that end, in this position paper, we investigate how causal and neurochaos learning approaches can be integrated together to produce better results, especially in domains that contain linked data. We propose an approach for this integration to enhance classification, prediction and reinforcement learning. We also propose a set of research questions that need to be investigated in order to make this integration a reality.