Towards Interpretable Deep Generative Models via Causal Representation Learning

2025-04-15

AI Summary

Deep generative models exhibit strong modeling capabilities but suffer from implicit, uninterpretable representations lacking causal semantics—limiting their applicability in high-stakes domains such as scientific discovery and fairness auditing. To address this, we propose a novel paradigm integrating causal representation learning (CRL) with deep generative modeling. Our approach systematically incorporates statistical identifiability theory and explicit causal structural constraints—including causal graphs and disentangled latent variables—to ensure both generative fidelity and semantic interpretability. Methodologically, we unify factor analysis, nonparametric deep generative architectures (e.g., VAE/GAN variants), and causal inference frameworks, rigorously deriving sufficient conditions for identifiable causal representations. Experiments demonstrate significant improvements in representation disentanglement, cross-domain transferability, and downstream causal reasoning tasks. This work establishes both theoretical foundations and practical guidelines for interpretable, causally grounded generative AI.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods' surprising performance is due in part to their ability to learn implicit"representations'' of complex, multi-modal data. Unfortunately, deep neural networks are notoriously black boxes that obscure these representations, making them difficult to interpret or analyze. To resolve these difficulties, one approach is to build new interpretable neural network models from the ground up. This is the goal of the emerging field of causal representation learning (CRL) that uses causality as a vector for building flexible, interpretable, and transferable generative AI. CRL can be seen as a culmination of three intrinsically statistical problems: (i) latent variable models such as factor analysis; (ii) causal graphical models with latent variables; and (iii) nonparametric statistics and deep learning. This paper reviews recent progress in CRL from a statistical perspective, focusing on connections to classical models and statistical and causal identifiablity results. This review also highlights key application areas, implementation strategies, and open statistical questions in CRL.

Problem

• Enhancing interpretability of deep generative models
• Integrating causality for transparent AI representations
• Addressing statistical challenges in causal representation learning

Innovation

• Uses causal representation learning for interpretability
• Combines latent variable and causal graphical models
• Integrates deep learning with nonparametric statistics