# CauSkelNet: Causal Representation Learning for Human Behaviour Analysis

arXiv: https://arxiv.org/abs/2409.15564 · HTML: https://mlsanity.com/paper/2409.15564/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Causality for Machine Learning](https://mlsanity.com/paper/1911.10500/) (2019)
- [Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View](https://mlsanity.com/paper/2112.02625/) (2021)
- [Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data](https://mlsanity.com/paper/2011.01776/) (2020)

## Built on by (most cited first, 3 of 3)

- [Task-Aware Delegation Cues for LLM Agents](https://mlsanity.com/paper/2603.11011/) (2026)
- [Uncertainty-Gated Generative Modeling](https://mlsanity.com/paper/2603.07753/) (2026)
- [Conceptual Belief-Informed Reinforcement Learning](https://mlsanity.com/paper/2410.01739/) (2024)

## Abstract (arXiv)

Traditional machine learning methods for movement recognition often struggle with limited model interpretability and a lack of insight into human movement dynamics. This study introduces a novel representation learning framework based on causal inference to address these challenges. Our two-stage approach combines the Peter-Clark (PC) algorithm and Kullback-Leibler (KL) divergence to identify and quantify causal relationships between human joints. By capturing joint interactions, the proposed causal Graph Convolutional Network (GCN) produces interpretable and robust representations. Experimental results on the EmoPain dataset demonstrate that the causal GCN outperforms traditional GCNs in accuracy, F1 score, and recall, particularly in detecting protective behaviors. This work contributes to advancing human motion analysis and lays a foundation for adaptive and intelligent healthcare solutions.