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.
Abstract (translated)
最近在生成式人工智能（AI）领域的进展依赖于机器学习技术，如深度学习和生成模型，在广泛的领域内实现了最先进的性能。这些方法之所以表现出色，部分原因在于它们能够学习复杂、多模态数据的隐含“表示”。不幸的是，深度神经网络因其固有的黑箱特性而难以揭示这些表示，从而使得解释或分析变得困难。为了解决这些问题，一种方法是从头开始构建新的可解释神经网络模型。这是新兴领域因果表征学习（CRL）的目标，该领域利用因果关系来建立灵活、可解释且具有迁移能力的生成式AI系统。 CRL可以被视为三种本质上统计问题的结合：(i)因子分析等隐变量模型；(ii)带有隐变量的因果图模型；以及(iii)非参数统计和深度学习。本文从统计学角度回顾了最近在CRL领域的进展，重点介绍了与经典模型的关系及统计和因果识别的结果。此外，该综述还强调了关键应用领域、实施策略以及CRL中的开放性统计问题。
URL
https://arxiv.org/abs/2504.11609
