mlsanityData to 7 Oct 2026

Emerging Synergies in Causality and Deep Generative Models: A Survey

arXiv 2301.12351 ↗First posted 29 Jan 2023cs.LG cs.AIcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 121 of its 249 references that are in the map. First posted on arXiv 29 Jan 2023.

Builds on

121 of 249 references.

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50 most cited of 121 All 121 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 1; 2026: 2.

10 papers.

  1. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  2. 2023Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformerscited by 35
  3. 2024On the Challenges and Opportunities in Generative AIcited by 31
  4. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
  5. 2024HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalizationcited by 8
  6. 2024Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challengescited by 8
  7. 2024Adapting Large Multimodal Models to Distribution Shifts: The Role of In-Context Learningcited by 4
  8. 2026From Statistical Fidelity to Clinical Consistency: Scalable Generation and Auditing of Synthetic Patient Trajectoriescited by 1
  9. 2025Shape Modeling of Longitudinal Medical Images: From Diffeomorphic Metric Mapping to Deep Learningcited by 0
  10. 2026Contextual Causality with Large Language Models: A Surveycited by 0