mlsanityData to 7 Oct 2026

Causal Machine Learning: A Survey and Open Problems

arXiv 2206.15475 ↗First posted 30 Jun 2022cs.LG stat.MEcited by 107

Not on the map. Why?

Abstract (arXiv)

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM). This perspective enables us to reason about the effects of changes to this process (interventions) and what would have happened in hindsight (counterfactuals). We categorize work in CausalML into five groups according to the problems they address: (1) causal supervised learning, (2) causal generative modeling, (3) causal explanations, (4) causal fairness, and (5) causal reinforcement learning. We systematically compare the methods in each category and point out open problems. Further, we review data-modality-specific applications in computer vision, natural language processing, and graph representation learning. Finally, we provide an overview of causal benchmarks and a critical discussion of the state of this nascent field, including recommendations for future work.

Key facts. Cited by 107 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jun 2022.

Builds on

0 of 0 references.

No references here yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 11; 2023: 34; 2024: 33; 2025: 19; 2026: 10.

107 papers.

Sort:

By area:

  1. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  2. 2023Challenges and Applications of Large Language Modelscited by 251
  3. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  4. 2023The MiniPile Challenge for Data-Efficient Language Modelscited by 74
  5. 2024Causal machine learning for predicting treatment outcomescited by 73
  6. 2022Less Data, More Knowledge: Building Next Generation Semantic Communication Networkscited by 61
  7. 2022SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generationcited by 56
  8. 2022Stop Wasting My Time! Saving Days of ImageNet and BERT Training with Latest Weight Averagingcited by 54
  9. 2023A Survey on Causal Reinforcement Learningcited by 43
  10. 2023BayeSeg: Bayesian Modeling for Medical Image Segmentation with Interpretable Generalizabilitycited by 42
  11. 2023Spawrious: A Benchmark for Fine Control of Spurious Correlation Biasescited by 41
  12. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
38 more
  1. 2024Foundation Models for Music: A Surveycited by 34
  2. 2024Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Spacecited by 33
  3. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  4. 2024Causal Concept Graph Models: Beyond Causal Opacity in Deep Learningcited by 28
  5. 2024Efficient Automated Circuit Discovery in Transformers using Contextual Decompositioncited by 23
  6. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  7. 2023Causal Reinforcement Learning: A Surveycited by 22
  8. 2024Benchmarking Counterfactual Image Generationcited by 22
  9. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 21
  10. 2023Causal Deep Learningcited by 20
  11. 2023Automated Cyber Defence: A Reviewcited by 19
  12. 2025Causally Reliable Concept Bottleneck Modelscited by 17
  13. 2023Interpretation of Time-Series Deep Models: A Surveycited by 15
  14. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  15. 2023TTIDA: Controllable Generative Data Augmentation via Text-to-Text and Text-to-Image Modelscited by 14
  16. 2022Applications of statistical causal inference in software engineeringcited by 12
  17. 2023Causal reasoning in typical computer vision taskscited by 12
  18. 2022Generalization in Neural Networks: A Broad Surveycited by 11
  19. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  20. 2024Do causal predictors generalize better to new domains?cited by 11
  21. 2022A Survey of Deep Causal Models and Their Industrial Applicationscited by 10
  22. 2022DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learningcited by 10
  23. 2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
  24. 2023DAG Learning on the Permutahedroncited by 9
  25. 2026Dynamics Within Latent Chain-of-Thought: An Empirical Study of Causal Structurecited by 9
  26. 2024A Systematic Review of Generalization Research in Medical Image Classificationcited by 8
  27. 2024Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challengescited by 8
  28. 2025Incentivizing Desirable Effort Profiles in Strategic Classification: The Role of Causality and Uncertaintycited by 8
  29. 2024Identifiability Guarantees for Causal Disentanglement from Purely Observational Datacited by 7
  30. 2023A Survey of Methods, Challenges and Perspectives in Causalitycited by 6
  31. 2023CFL: Causally Fair Language Models Through Token-level Attribute Controlled Generationcited by 6
  32. 2025Unveiling and Causalizing CoT: A Causal Pespectivecited by 6
  33. 2023Exploiting Causal Graph Priors with Posterior Sampling for Reinforcement Learningcited by 5
  34. 2024Focus On This, Not That! Steering LLMs with Adaptive Feature Specificationcited by 5
  35. 2025JAM: Controllable and Responsible Text Generation via Causal Reasoning and Latent Vector Manipulationcited by 5
  36. 2022Regulating Gatekeeper AI and Data: Transparency, Access, and Fairness under the DMA, the GDPR, and beyondcited by 4
  37. 2023Competence-Based Analysis of Language Modelscited by 4
  38. 2023Selecting Robust Features for Machine Learning Applications using Multidata Causal Discoverycited by 4

50 most cited of 107 All 107 (JSON)