In the field of artificial intelligence (AI), the quest to understand and model data-generating processes (DGPs) is of paramount importance. Deep generative models (DGMs) have proven adept in capturing complex data distributions but often fall short in generalization and interpretability. On the other hand, causality offers a structured lens to comprehend the mechanisms driving data generation and highlights the causal-effect dynamics inherent in these processes. While causality excels in interpretability and the ability to extrapolate, it grapples with intricacies of high-dimensional spaces. Recognizing the synergistic potential, we delve into the confluence of causality and DGMs. We elucidate the integration of causal principles within DGMs, investigate causal identification using DGMs, and navigate an emerging research frontier of causality in large-scale generative models, particularly generative large language models (LLMs). We offer insights into methodologies, highlight open challenges, and suggest future directions, positioning our comprehensive review as an essential guide in this swiftly emerging and evolving area.

翻译：在人工智能领域，理解和建模数据生成过程具有至关重要的意义。深度生成模型已被证明能够有效捕捉复杂的数据分布，但在泛化能力和可解释性方面往往存在不足。另一方面，因果推断为理解数据生成的驱动机制提供了结构化视角，并揭示了这些过程中固有的因果效应动态。尽管因果推断在可解释性和外推能力方面表现卓越，但其在处理高维空间复杂性时面临挑战。认识到两者间的协同潜力，本文深入探讨了因果推断与深度生成模型的交汇融合。我们系统阐述了因果原理在深度生成模型中的整合方法，探究了基于深度生成模型的因果识别技术，并深入剖析了大规模生成模型（特别是生成式大语言模型）中因果推断这一新兴研究前沿。通过对方法论进行解析，指明当前面临的开放挑战并提出未来研究方向，本综述旨在为这一快速兴起并持续发展的领域提供重要参考指南。