Structural Equation-VAE: Disentangled Latent Representations for Tabular Data

• URL: http://arxiv.org/abs/2508.06347v2
• Date: Sat, 16 Aug 2025 10:27:15 GMT
• Title: Structural Equation-VAE: Disentangled Latent Representations for Tabular Data
• Authors: Ruiyu Zhang, Ce Zhao, Xin Zhao, Lin Nie, Wai-Fung Lam,
• Abstract summary: We introduce SE-VAE (Structural Equation-Variational Autoencoder), a novel architecture that embeds measurement structure directly into the design of a variational autoencoder.<n>Inspired by structural equation modeling, SE-VAE aligns latent subspaces with known indicator groupings and introduces a global nuisance latent to isolate construct-specific confounding variation.<n> SE-VAE consistently outperforms alternatives in factor recovery, interpretability, and robustness to nuisance variation.
• Score: 4.101599614979332
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning interpretable latent representations from tabular data remains a challenge in deep generative modeling. We introduce SE-VAE (Structural Equation-Variational Autoencoder), a novel architecture that embeds measurement structure directly into the design of a variational autoencoder. Inspired by structural equation modeling, SE-VAE aligns latent subspaces with known indicator groupings and introduces a global nuisance latent to isolate construct-specific confounding variation. This modular architecture enables disentanglement through design rather than through statistical regularizers alone. We evaluate SE-VAE on a suite of simulated tabular datasets and benchmark its performance against a series of leading baselines using standard disentanglement metrics. SE-VAE consistently outperforms alternatives in factor recovery, interpretability, and robustness to nuisance variation. Ablation results reveal that architectural structure, rather than regularization strength, is the key driver of performance. SE-VAE offers a principled framework for white-box generative modeling in scientific and social domains where latent constructs are theory-driven and measurement validity is essential.

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