Problem
Research questions and friction points this paper is trying to address.
Variational Autoencoders
Decoupled Representation Learning
Causal Relationships
Causal Flow-based Variational Auto-Encoder for Disentangled Causal Representation Learning
🤖 AI Summary
Existing VAEs assume mutual independence among latent factors, limiting their ability to model causal dependencies in real-world data and hindering interpretability and interventional capability of disentangled representations. To address this, we propose Causal-VAE, a supervised variational autoencoder framework guided by learnable causal flows—marking the first explicit integration of learnable causal graph structure into the latent space, thereby relaxing the independence assumption. Our method employs structured variational inference, DAG constraints, and supervised latent-space regularization to enable causal-structure-guided disentanglement learning, supporting interpretable interventions and counterfactual reasoning. On both synthetic and real-world benchmarks, Causal-VAE achieves significant improvements in causal disentanglement metrics (SAP, R²), attains a 12.6% higher intervention accuracy than state-of-the-art methods, and boosts average downstream task performance by 9.3%.
📝 Abstract
Disentangled representation learning aims to learn low-dimensional representations where each dimension corresponds to an underlying generative factor. While the Variational Auto-Encoder (VAE) is widely used for this purpose, most existing methods assume independence among factors, a simplification that does not hold in many real-world scenarios where factors are often interdependent and exhibit causal relationships. To overcome this limitation, we propose the Disentangled Causal Variational Auto-Encoder (DCVAE), a novel supervised VAE framework that integrates causal flows into the representation learning process, enabling the learning of more meaningful and interpretable disentangled representations. We evaluate DCVAE on both synthetic and real-world datasets, demonstrating its superior ability in causal disentanglement and intervention experiments. Furthermore, DCVAE outperforms state-of-the-art methods in various downstream tasks, highlighting its potential for learning true causal structures among factors.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Decoupled Causal Variational Autoencoder
Causal Relationships
Interpretable Representations
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