1 Introduction
2 Background
2.1 Causality
2.2 Deep Generative Models
3 Integrating Causal Principles in DGMs
3.1 Rationale for Causal Integration in DGMs
3.2 Setting the Stage for Causal Generative Models
3.3 Classifying Methods of Causal Integration
4 Identifying Causality via DGMs
4.1 Causal Discovery through DGMs
4.2 Counterfactual Inference Leveraging DGMs
4.3 Causal Representation Learning using DGMs
5 Exploring Causality Within Large-Scale DGMs: An Emerging Frontier
5.1 Assessing Causal Capabilities in Current LLMs: Memorization or Reasoning?
5.2 Advancing Causal Research Through LLMs
6 Trustworthy Properties
6.1 Generalization
6.2 Fairness
6.3 Interpretability
7 Applications
7.1 Computer Vision
7.2 Health Science
7.3 Social Science
8 Discussion
8.1 Understanding Generated Counterfactuals
8.2 Challenges with Large-scale Causal Graphs
8.3 Incorporating Causal Discovery for Causal Generative Models
8.4 Causal Learning within Large-scale Generative Models
9 Conclusion