Towards Interpretable Deep Generative Models via Causal Representation Learning
通过因果表征学习构建可解释的深度生成模型AI 解读 ↓
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通过因果表征学习构建可解释的深度生成模型
- 研究问题
- 如何使深度生成模型学到的隐含表征更易解释与迁移，因果表征学习与经典统计模型之间有哪些联系和识别问题？
- 方法与思路
- 作者从统计视角介绍因果表征学习，将潜变量模型、含潜变量的因果图模型及非参数统计与深度学习联系起来。
- 材料陈述的贡献
- 摘要所述贡献是梳理该领域的统计基础、统计与因果可识别性结果，并介绍应用方向、实现策略和仍待解决的统计问题。
- 阅读关注点
- 应核对统计可识别性与因果可识别性的区别，各类结果需要何种观测或干预信息，以及表征解释与迁移主张的适用范围。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核验；摘要以介绍和梳理为主，未说明新的估计量、定理或经验证实的性能提升。
原文摘要
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 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.