Deep generative models have shown tremendous success in data density estimation and data generation from finite samples. While these models have shown impressive performance by learning correlations among features in the data, some fundamental shortcomings are their lack of explainability, the tendency to induce spurious correlations, and poor out-of-distribution extrapolation. In an effort to remedy such challenges, one can incorporate the theory of causality in deep generative modeling. Structural causal models (SCMs) describe data-generating processes and model complex causal relationships and mechanisms among variables in a system. Thus, SCMs can naturally be combined with deep generative models. Causal models offer several beneficial properties to deep generative models, such as distribution shift robustness, fairness, and interoperability. We provide a technical survey on causal generative modeling categorized into causal representation learning and controllable counterfactual generation methods. We focus on fundamental theory, formulations, drawbacks, datasets, metrics, and applications of causal generative models in fairness, privacy, out-of-distribution generalization, and precision medicine. We also discuss open problems and fruitful research directions for future work in the field.

翻译：深度生成模型在有限样本的数据密度估计与数据生成方面取得了巨大成功。虽然这些模型通过学习数据特征间的相关性展现了卓越性能，但其根本缺陷包括缺乏可解释性、易诱导虚假相关性以及分布外泛化能力差。为应对此类挑战，可将因果理论融入深度生成建模。结构因果模型（SCM）描述了数据生成过程，并建模系统中变量间复杂的因果关系与机制，因此天然适用于与深度生成模型结合。因果模型为深度生成模型提供了多项有益特性，如分布偏移鲁棒性、公平性与可解释性。我们针对因果生成建模领域进行技术综述，将其归纳为因果表征学习与可控反事实生成两大方法。重点阐述因果生成模型的基础理论、形式化定义、局限性、数据集、评估指标及其在公平性、隐私保护、分布外泛化与精准医学中的应用，并探讨该领域待解决的开放问题与未来研究方向。