Causal Representation Learning for Enhancing Interpretability and Reliability in Machine Learning Models for High Dimensional and Noisy Data
Keywords:
Causal Representation Learning, Interpretability, Reliability, High-Dimensional Data, Noisy Data, Machine LearningAbstract
Machine learning (ML) models often struggle with high-dimensional and noisy data, leading to challenges in interpretability and reliability. Causal representation learning (CRL) aims to address these issues by disentangling underlying causal structures from observed data, allowing models to make robust and generalizable predictions. This paper explores the role of CRL in enhancing ML models’ performance under complex data conditions. We review recent literature, discuss methodological advancements, and present experimental insights demonstrating CRL's efficacy. The findings suggest that CRL significantly improves interpretability and reduces vulnerability to spurious correlations, offering a promising direction for future research.
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Copyright (c) 2025 Silvia Martelli (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
