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 paper introduces CRL from a statistical perspective, focusing on connections to classical models as well as statistical and causal identifiability results. We also highlights key application areas, implementation strategies, and open statistical questions.

翻译：生成式人工智能（AI）的最新进展依赖于深度学习和生成建模等机器学习技术，在广泛领域中实现了最先进的性能。这些方法之所以表现出令人惊讶的性能，部分原因在于其能够学习复杂多模态数据的隐式“表征”。然而，深度神经网络作为众所周知的“黑箱”，掩盖了这些表征，使其难以解释或分析。为解决这些难题，一种途径是从头构建新型可解释神经网络模型。这正是新兴的因果表征学习（CRL）领域的目标，该领域利用因果关系作为构建灵活、可解释且可迁移的生成式AI的载体。CRL可视为三个本质统计思想的综合：（i）因子分析等潜变量模型；（ii）含潜变量的因果图模型；（iii）非参数统计与深度学习。本文从统计学视角介绍CRL，重点关注其与经典模型的联系，以及统计与因果可识别性结果。同时，我们着重阐述了关键应用领域、实施策略以及开放的统计学问题。