mlsanityData to 7 Oct 2026

Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges

arXiv 2405.05025 ↗First posted 8 May 2024stat.ML cs.LGcited by 8

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Abstract (arXiv)

This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational data within known causal structures. It delves into the characteristics of DSCMs by analyzing the hypotheses, guarantees, and applications inherent to the underlying deep learning components and structural causal models, fostering a finer understanding of their capabilities and limitations in addressing different counterfactual queries. Furthermore, it highlights the challenges and open questions in the field of deep structural causal modeling. It sets the stages for researchers to identify future work directions and for practitioners to get an overview in order to find out the most appropriate methods for their needs.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 33 of its 54 references that are in the map. First posted on arXiv 8 May 2024.

Builds on

33 of 54 references.

References · 33

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  4. 2020Deep Structural Causal Models for Tractable Counterfactual Inferencecited by 210
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  6. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  7. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  8. 2021Counterfactual Generative Networkscited by 111
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  11. 2020Causal Autoregressive Flowscited by 97
  12. 2021The Causal-Neural Connection: Expressiveness, Learnability, and Inferencecited by 97
21 more
  1. 2020Weakly Supervised Disentangled Generative Causal Representation Learningcited by 67
  2. 2021Partial Counterfactual Identification from Observational and Experimental Datacited by 64
  3. 2018Morpho-MNIST: Quantitative Assessment and Diagnostics for Representation Learningcited by 56
  4. 2020Evaluating and Mitigating Bias in Image Classifiers: A Causal Perspective Using Counterfactualscited by 56
  5. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 54
  6. 2023Causal normalizing flows: from theory to practicecited by 49
  7. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 42
  8. 2021Relating Graph Neural Networks to Structural Causal Modelscited by 39
  9. 2021Compositional Abstraction Error and a Category of Causal Modelscited by 32
  10. 2020Structural Causal Models Are (Solvable by) Credal Networkscited by 22
  11. 2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
  12. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
  13. 2020Probabilistic Reasoning across the Causal Hierarchycited by 19
  14. 2023A Neural Framework for Generalized Causal Sensitivity Analysiscited by 17
  15. 2022Tractable Uncertainty for Structure Learningcited by 15
  16. 2023Partial Counterfactual Identification of Continuous Outcomes with a Curvature Sensitivity Modelcited by 14
  17. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  18. 2023Causal Fairness under Unobserved Confounding: A Neural Sensitivity Frameworkcited by 10
  19. 2020Causal Inference with Deep Causal Graphscited by 5
  20. 2023Where and How to Attack? A Causality-Inspired Recipe for Generating Counterfactual Adversarial Examplescited by 3
  21. 2023A Guide for Practical Use of ADMG Causal Data Augmentationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 1.

8 papers.

  1. 2025Diffusion Counterfactual Generation with Semantic Abductioncited by 8
  2. 2025Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoptioncited by 6
  3. 2025Interpretable Neural Causal Models with TRAM-DAGscited by 4
  4. 2025Towards Causal Market Simulatorscited by 2
  5. 2025ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencodercited by 1
  6. 2025Unsupervised Structural-Counterfactual Generation under Domain Shiftcited by 0
  7. 2025Learning Counterfactually Fair Models via Improved Generation with Neural Causal Modelscited by 0
  8. 2026Causal Transfer in Medical Image Analysiscited by 0