mlsanityData to 7 Oct 2026

Invariant Causal Mechanisms through Distribution Matching

arXiv 2206.11646 ↗First posted 23 Jun 2022cs.LG stat.MLcited by 31

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Abstract (arXiv)

Learning representations that capture the underlying data generating process is a key problem for data efficient and robust use of neural networks. One key property for robustness which the learned representation should capture and which recently received a lot of attention is described by the notion of invariance. In this work we provide a causal perspective and new algorithm for learning invariant representations. Empirically we show that this algorithm works well on a diverse set of tasks and in particular we observe state-of-the-art performance on domain generalization, where we are able to significantly boost the score of existing models.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 34 of its 55 references that are in the map. First posted on arXiv 23 Jun 2022.

Builds on

34 of 55 references.

References · 34

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2015Domain-Adversarial Training of Neural Networkscited by 3,023
  4. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  5. 2018Moment Matching for Multi-Source Domain Adaptationcited by 1,327
  6. 2019Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalizationcited by 1,232
  7. 2016Deep CORAL: Correlation Alignment for Deep Domain Adaptationcited by 1,194
  8. 2017Deep Hashing Network for Unsupervised Domain Adaptationcited by 1,181
  9. 2020In Search of Lost Domain Generalizationcited by 1,042
  10. 2017Deeper, Broader and Artier Domain Generalizationcited by 983
  11. 2017Learning to Generalize: Meta-Learning for Domain Generalizationcited by 762
  12. 2020Out-of-Distribution Generalization via Risk Extrapolation (REx)cited by 737
22 more
  1. 2018Recognition in Terra Incognitacited by 655
  2. 2013Domain Generalization via Invariant Feature Representationcited by 622
  3. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  4. 2020Self-Challenging Improves Cross-Domain Generalizationcited by 426
  5. 2015Domain Generalization for Object Recognition with Multi-task Autoencoderscited by 253
  6. 2018DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptationcited by 212
  7. 2021Towards Causal Representation Learningcited by 207
  8. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  9. 2015Censoring Representations with an Adversarycited by 191
  10. 2020Improve Unsupervised Domain Adaptation with Mixup Trainingcited by 185
  11. 2019On the Fairness of Disentangled Representationscited by 144
  12. 2019Wasserstein Fair Classificationcited by 131
  13. 2017Controllable Invariance through Adversarial Feature Learningcited by 127
  14. 2018Anchor regression: heterogeneous data meets causalitycited by 116
  15. 2019Generalizing to unseen domains via distribution matchingcited by 109
  16. 2018Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustnesscited by 108
  17. 2020On Disentangled Representations Learned From Correlated Datacited by 94
  18. 2019Mitigating Information Leakage in Image Representations: A Maximum Entropy Approachcited by 48
  19. 2017Iterative Random Forests to detect predictive and stable high-order interactionscited by 42
  20. 2019Stabilizing Variable Selection and Regressioncited by 31
  21. 2020Representation via Representations: Domain Generalization via Adversarially Learned Invariant Representationscited by 26
  22. 2018Learning stable and predictive structures in kinetic systems: Benefits of a causal approachcited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 10; 2024: 10; 2025: 6; 2026: 2.

31 papers.

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  1. 2023Spawrious: A Benchmark for Fine Control of Spurious Correlation Biasescited by 41
  2. 2023Domain Generalization in Computational Pathology: Survey and Guidelinescited by 29
  3. 2022Domain Generalization -- A Causal Perspectivecited by 14
  4. 2023Domain Generalisation via Risk Distribution Matchingcited by 14
  5. 2022Back-to-Bones: Rediscovering the Role of Backbones in Domain Generalizationcited by 12
  6. 2024HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalizationcited by 8
  7. 2024Controlled Training Data Generation with Diffusion Modelscited by 8
  8. 2023Non-parametric identifiability and sensitivity analysis of synthetic control modelscited by 5
  9. 2023CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Gradingcited by 5
  10. 2025Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalizationcited by 5
  11. 2025When Shift Happens - Confounding Is to Blamecited by 5
  12. 2023Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalizationcited by 4
19 more
  1. 2024Causally Inspired Regularization Enables Domain General Representationscited by 4
  2. 2023Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneitycited by 3
  3. 2024How Does Distribution Matching Help Domain Generalization: An Information-theoretic Analysiscited by 3
  4. 2024Benchmarking Domain Generalization Algorithms in Computational Pathologycited by 3
  5. 2023Robustness May be More Brittle than We Think under Different Degrees of Distribution Shiftscited by 2
  6. 2024UnLearning from Experience to Avoid Spurious Correlationscited by 2
  7. 2025Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergiescited by 2
  8. 2023Learning Invariant Representations with a Nonparametric Nadaraya-Watson Headcited by 1
  9. 2023Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approachcited by 1
  10. 2024Revisiting Spurious Correlation in Domain Generalizationcited by 1
  11. 2024C3DG: Conditional Domain Generalization for Hyperspectral Imagery Classification with Convergence and Constrained-risk Theoriescited by 1
  12. 2024DICS: Find Domain-Invariant and Class-Specific Features for Out-of-Distribution Generalizationcited by 1
  13. 2025Robust Context-Aware Object Recognitioncited by 1
  14. 2025Exploring Scale Shift in Crowd Localization under the Context of Domain Generalizationcited by 1
  15. 2026A2: Smaller Self-Supervised ViTs Localize Better than Larger Onescited by 1
  16. 2022Direct-Effect Risk Minimization for Domain Generalizationcited by 0
  17. 2024Bringing the Context Back into Object Recognition, Robustlycited by 0
  18. 2025Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learningcited by 0
  19. 2026Assessing Distribution Shift in Human Activity Recognition for Domain Generalizationcited by 0