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SEMI-CAVA: A Causal Variational Approach to Semi-Supervised Learning

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This approach utilizes causality to enhance latent representations in semi-supervised learning, suggesting new strategies for class imbalance.

The proposed model achieves state-of-the-art performance on various medical datasets and standard benchmarks.
Under specific assumptions, the model guarantees that latent representations align with true causal factors.
Insights from causality and variational inference are creatively combined to improve semi-supervised learning.
The incorporation of the Mixup strategy enhances the model's ability to manage class imbalance effectively.

Saha et al. (2025) studied this question.

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This approach utilizes causality to enhance latent representations in semi-supervised learning, suggesting new strategies for class imbalance.