mlsanityData to 14 Oct 2026

Visual Causal Feature Learning

arXiv 1412.2309 ↗First posted 7 Dec 2014stat.ML cs.AI cs.CVcited by 59

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Abstract (arXiv)

We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed from micro-variables. We prove the Causal Coarsening Theorem, which allows us to gain causal knowledge from observational data with minimal experimental effort. The theorem provides a connection to standard inference techniques in machine learning that identify features of an image that correlate with, but may not cause, the target behavior. Finally, we propose an active learning scheme to learn a manipulator function that performs optimal manipulations on the image to automatically identify the visual cause of a target behavior. We illustrate our inference and learning algorithms in experiments based on both synthetic and real data.

Key facts. Cited by 59 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Dec 2014.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 14; 2023: 15; 2024: 6; 2025: 10; 2026: 3.

59 papers.

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  1. 2023Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretabilitycited by 205
  2. 2022Weakly supervised causal representation learningcited by 140
  3. 2022CITRIS: Causal Identifiability from Temporal Intervened Sequencescited by 133
  4. 2021Distilling Causal Effect of Data in Class-Incremental Learningcited by 117
  5. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 106
  6. 2021Interventional Video Grounding with Dual Contrastive Learningcited by 94
  7. 2021Towards Principled Disentanglement for Domain Generalizationcited by 91
  8. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
  9. 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 66
  10. 2021Learning latent causal graphs via mixture oraclescited by 55
  11. 2021Human Trajectory Prediction via Counterfactual Analysiscited by 42
  12. 2022Causal Effect Identification in Cluster DAGscited by 36
38 more
  1. 2023Learning nonparametric latent causal graphs with unknown interventionscited by 34
  2. 2021Compositional Abstraction Error and a Category of Causal Modelscited by 33
  3. 2023Neural Algorithmic Reasoning with Causal Regularisationcited by 31
  4. 2022OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semanticscited by 24
  5. 2024Robust Emotion Recognition in Context Debiasingcited by 24
  6. 2022Causal Abstraction with Soft Interventionscited by 23
  7. 2022Image-based Treatment Effect Heterogeneitycited by 20
  8. 2022Counterfactual Intervention Feature Transfer for Visible-Infrared Person Re-identificationcited by 18
  9. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  10. 2021The Blessings of Unlabeled Background in Untrimmed Videoscited by 16
  11. 2023Targeted Reduction of Causal Modelscited by 15
  12. 2023Causal models in string diagramscited by 14
  13. 2022On the Equivalence of Causal Models: A Category-Theoretic Approachcited by 13
  14. 2021Towards Unbiased Visual Emotion Recognition via Causal Interventioncited by 11
  15. 2022Explanatory Paradigms in Neural Networkscited by 11
  16. 2022Beyond Object Recognition: A New Benchmark towards Object Concept Learningcited by 11
  17. 2023Toward Falsifying Causal Graphs Using a Permutation-Based Testcited by 11
  18. 2023Enhancing Adversarial Contrastive Learning via Adversarial Invariant Regularizationcited by 10
  19. 2023Integrating Earth Observation Data into Causal Inference: Challenges and Opportunitiescited by 8
  20. 2022Phenomenological Causalitycited by 7
  21. 2024Robust Training of Federated Models with Extremely Label Deficiencycited by 7
  22. 2023Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and their Impactcited by 6
  23. 2023RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in Object-centric Learningcited by 6
  24. 2025What is causal about causal models and representations?cited by 6
  25. 2025Relaxing partition admissibility in Cluster-DAGs: a causal calculus with arbitrary variable clusteringcited by 5
  26. 2021iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretabilitycited by 4
  27. 2021Encoding Causal Macrovariablescited by 4
  28. 2022Estimating Causal Effects Under Image Confounding Bias with an Application to Poverty in Africacited by 4
  29. 2023Neuro-Causal Factor Analysiscited by 4
  30. 2024The Causal Information Bottleneck and Optimal Causal Variable Abstractionscited by 4
  31. 2025Combining Causal Models for More Accurate Abstractions of Neural Networkscited by 4
  32. 2025Exploratory Causal Inference in SAEncecited by 4
  33. 2022Treatment Learning Causal Transformer for Noisy Image Classificationcited by 3
  34. 2023Adversarial Attacks on Image Classification Models: Analysis and Defensecited by 2
  35. 2024Causal Imputation for Counterfactual SCMs: Bridging Graphs and Latent Factor Modelscited by 2
  36. 2024Causality-Driven Audits of Model Robustnesscited by 2
  37. 2025CauSight: Learning to Supersense for Visual Causal Discoverycited by 2
  38. 2025Trust Me, I Know the Way: Predictive Uncertainty in the Presence of Shortcut Learningcited by 1

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