Toward Interpretable Deep Generative Models via Causal Representation Learning
本文从统计学角度介绍因果表示学习（CRL），将其视为潜变量模型、因果图模型与非参数统计及深度学习的融合，旨在构建可解释、可迁移的生成式AI，并讨论识别性、应用及开放问题。
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.