Toward Interpretable Deep Generative Models via Causal Representation Learning

• 成果类型: Article
• 署名作者: Moran, Gemma; Aragam, Bryon
• 署名单位: Rutgers University System; Rutgers University New Brunswick; University of Chicago
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2026.2620154
• 发表日期: 2026-01-02
• 页码: 259-275
• 关键词: Causality; Deep learning; Generative models; Latent Variable Models; Machine Learning; Independent Component Analysis; uniqueness; crispr; ica

摘要

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 synthesis of three intrinsically statistical ideas: (i) latent variable models such as factor analysis; (ii) causal graphical models with latent variables; and (iii) nonparametric statistics and deep learning. This article introduces CRL from a statistical perspective, focusing on connections to classical models as well as statistical and causal identifiability results. We also highlight key application areas, implementation strategies, and open statistical questions. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.