Deep generative models have gained popularity in recent years due to their ability to accurately replicate inherent empirical distributions and yield novel samples. In particular, certain advances are proposed wherein the model engenders data examples following specified attributes. Nevertheless, several challenges still exist and are to be overcome, i.e., difficulty in extrapolating out-of-sample data and insufficient learning of disentangled representations. Structural causal models (SCMs), on the other hand, encapsulate the causal factors that govern a generative process and characterize a generative model based on causal relationships, providing crucial insights for addressing the current obstacles in deep generative models. In this paper, we present a comprehensive survey of Causal deep Generative Models (CGMs), which combine SCMs and deep generative models in a way that boosts several trustworthy properties such as robustness, fairness, and interpretability. We provide an overview of the recent advances in CGMs, categorize them based on generative types, and discuss how causality is introduced into the family of deep generative models. We also explore potential avenues for future research in this field.

翻译：深度生成模型近年来因其能够准确再现经验分布并生成新颖样本而广受欢迎。特别是，已有若干进展提出模型能根据指定属性生成数据实例。然而，仍存在若干待克服的挑战，例如外推样本外数据的困难以及解耦表示学习的不足。另一方面，结构因果模型（SCMs）封装了支配生成过程的因果因素，并基于因果关系刻画生成模型，为解决深度生成模型当前的障碍提供了关键见解。本文全面综述了因果深度生成模型（CGMs），这类模型通过融合SCMs与深度生成模型，提升了鲁棒性、公平性和可解释性等可信属性。我们概述了CGMs的最新进展，基于生成类型对其进行分类，并讨论了因果性如何引入深度生成模型家族。此外，我们还探索了该领域未来研究的潜在方向。