mlsanityData to 7 Oct 2026

Deep Structural Causal Models for Tractable Counterfactual Inference

arXiv 2006.06485 ↗First posted 11 Jun 2020stat.ML cs.LGcited by 210

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Abstract (arXiv)

We formulate a general framework for building structural causal models (SCMs) with deep learning components. The proposed approach employs normalising flows and variational inference to enable tractable inference of exogenous noise variables - a crucial step for counterfactual inference that is missing from existing deep causal learning methods. Our framework is validated on a synthetic dataset built on MNIST as well as on a real-world medical dataset of brain MRI scans. Our experimental results indicate that we can successfully train deep SCMs that are capable of all three levels of Pearl's ladder of causation: association, intervention, and counterfactuals, giving rise to a powerful new approach for answering causal questions in imaging applications and beyond. The code for all our experiments is available at https://github.com/biomedia-mira/deepscm.

Key facts. Cited by 210 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jun 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 20; 2022: 39; 2023: 45; 2024: 32; 2025: 39; 2026: 35.

210 papers.

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  1. 2023HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Datacited by 124
  2. 2022Diffusion Visual Counterfactual Explanationscited by 96
  3. 2022Deep End-to-end Causal Inferencecited by 93
  4. 2022Diffusion Causal Models for Counterfactual Estimationcited by 87
  5. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  6. 2021Learning from Counterfactual Links for Link Predictioncited by 69
  7. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  8. 2021Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanationscited by 56
  9. 2022Neural Causal Models for Counterfactual Identification and Estimationcited by 54
  10. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  11. 2022What is Healthy? Generative Counterfactual Diffusion for Lesion Localizationcited by 52
  12. 2023Causal normalizing flows: from theory to practicecited by 49
38 more
  1. 2021Learning Disentangled Representations in the Imaging Domaincited by 46
  2. 2022A Causal Lens for Controllable Text Generationcited by 45
  3. 2023Counterfactual Identifiability of Bijective Causal Modelscited by 42
  4. 2021Relating Graph Neural Networks to Structural Causal Modelscited by 39
  5. 2023Measuring axiomatic soundness of counterfactual image modelscited by 34
  6. 2024On the Challenges and Opportunities in Generative AIcited by 31
  7. 2022Sparse Visual Counterfactual Explanations in Image Spacecited by 30
  8. 2022Rhino: Deep Causal Temporal Relationship Learning With History-dependent Noisecited by 30
  9. 2023GaitGCI: Generative Counterfactual Intervention for Gait Recognitioncited by 30
  10. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  11. 2021A Critical Look at the Consistency of Causal Estimation With Deep Latent Variable Modelscited by 29
  12. 2022Counterfactual harmcited by 29
  13. 2022Generating Realistic Brain MRIs via a Conditional Diffusion Probabilistic Modelcited by 28
  14. 2023RadEdit: stress-testing biomedical vision models via diffusion image editingcited by 28
  15. 2022D'ARTAGNAN: Counterfactual Video Generationcited by 27
  16. 2023Self-Interpretable Time Series Prediction with Counterfactual Explanationscited by 24
  17. 2023Modeling Causal Mechanisms with Diffusion Models for Interventional and Counterfactual Queriescited by 23
  18. 2024Benchmarking Counterfactual Image Generationcited by 22
  19. 2021A Structural Causal Model for MR Images of Multiple Sclerosiscited by 21
  20. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
  21. 2022Image-based Treatment Effect Heterogeneitycited by 20
  22. 2022Learning Attention Propagation for Compositional Zero-Shot Learningcited by 20
  23. 2023Causal Deep Learningcited by 20
  24. 2024Counterfactual Image Editingcited by 20
  25. 2022Feature robustness and sex differences in medical imaging: a case study in MRI-based Alzheimer's disease detectioncited by 19
  26. 2021Counterfactual Explanations as Interventions in Latent Spacecited by 18
  27. 2021Estimating Categorical Counterfactuals via Deep Twin Networkscited by 18
  28. 2021VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queriescited by 18
  29. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  30. 2023Counterfactual (Non-)identifiability of Learned Structural Causal Modelscited by 17
  31. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17
  32. 2024Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Modelscited by 17
  33. 2022Personalized Public Policy Analysis in Social Sciences using Causal-Graphical Normalizing Flowscited by 16
  34. 2022CaRTS: Causality-driven Robot Tool Segmentation from Vision and Kinematics Datacited by 16
  35. 2021Challenges for machine learning in clinical translation of big data imaging studiescited by 15
  36. 2021Data synthesis and adversarial networks: A review and meta-analysis in cancer imagingcited by 15
  37. 2021Harmonization with Flow-based Causal Inferencecited by 14
  38. 2022medigan: a Python library of pretrained generative models for medical image synthesiscited by 14

50 most cited of 210 All 210 (JSON)