mlsanityData to 14 Oct 2026

Causality for Machine Learning

arXiv 1911.10500 ↗First posted 24 Nov 2019cs.LG cs.AI stat.MLcited by 243

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Abstract (arXiv)

Graphical causal inference as pioneered by Judea Pearl arose from research on artificial intelligence (AI), and for a long time had little connection to the field of machine learning. This article discusses where links have been and should be established, introducing key concepts along the way. It argues that the hard open problems of machine learning and AI are intrinsically related to causality, and explains how the field is beginning to understand them.

Key facts. Cited by 243 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 49; 2022: 58; 2023: 52; 2024: 33; 2025: 29; 2026: 21.

243 papers.

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  1. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,920
  2. 2022Using cognitive psychology to understand GPT-3cited by 398
  3. 2021Self-Supervised Learning with Data Augmentations Provably Isolates Content from Stylecited by 312
  4. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  5. 2021Towards Causal Representation Learningcited by 209
  6. 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
  7. 2021A Survey of Zero-shot Generalisation in Deep Reinforcement Learningcited by 170
  8. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  9. 2023Causal Parrots: Large Language Models May Talk Causality But Are Not Causalcited by 150
  10. 2022Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphscited by 144
  11. 2022Causality Inspired Representation Learning for Domain Generalizationcited by 138
  12. 2021Distilling Causal Effect of Data in Class-Incremental Learningcited by 117
38 more
  1. 2021Counterfactual Generative Networkscited by 111
  2. 2023Nonparametric Identifiability of Causal Representations from Unknown Interventionscited by 106
  3. 2021Counterfactual Zero-Shot and Open-Set Visual Recognitioncited by 95
  4. 2022Amortized Inference for Causal Structure Learningcited by 90
  5. 2021Size-Invariant Graph Representations for Graph Classification Extrapolationscited by 84
  6. 2021Generalization and Robustness Implications in Object-Centric Learningcited by 81
  7. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  8. 2022Diversify and Disambiguate: Learning From Underspecified Datacited by 56
  9. 2023High Fidelity Image Counterfactuals with Probabilistic Causal Modelscited by 53
  10. 2021Instance-dependent Label-noise Learning under a Structural Causal Modelcited by 50
  11. 2023Score-based Causal Representation Learning with Interventionscited by 49
  12. 2021AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learningcited by 48
  13. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 47
  14. 2022A Causal Lens for Controllable Text Generationcited by 45
  15. 2022Out-of-distribution Generalization with Causal Invariant Transformationscited by 45
  16. 2022Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learningcited by 44
  17. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  18. 2021Neural graphical modelling in continuous-time: consistency guarantees and algorithmscited by 42
  19. 2022From Statistical to Causal Learningcited by 40
  20. 2022Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modelingcited by 34
  21. 2021Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responsescited by 32
  22. 2024The Essential Role of Causality in Foundation World Models for Embodied AIcited by 32
  23. 2021Action-Sufficient State Representation Learning for Control with Structural Constraintscited by 31
  24. 2022Can Foundation Models Talk Causality?cited by 30
  25. 2022FaiRR: Faithful and Robust Deductive Reasoning over Natural Languagecited by 29
  26. 2022Neuro-Symbolic Procedural Planning with Commonsense Promptingcited by 28
  27. 2022Discovering Agentscited by 28
  28. 2022Certified Robustness Against Natural Language Attacks by Causal Interventioncited by 24
  29. 2021Causal Navigation by Continuous-time Neural Networkscited by 23
  30. 2021CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoningcited by 23
  31. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  32. 2021Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Modelscited by 22
  33. 2023Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Featurescited by 21
  34. 2021Adversarial Training is Not Ready for Robot Learningcited by 20
  35. 2022Learning Attention Propagation for Compositional Zero-Shot Learningcited by 20
  36. 2026Sensorimotor World Models: Perception for Action via Inverse Dynamicscited by 20
  37. 2021Embracing the Disharmony in Medical Imaging: A Simple and Effective Framework for Domain Adaptationcited by 19
  38. 2021VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queriescited by 19

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