mlsanityData to 7 Oct 2026

Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution

arXiv 1801.04016 ↗First posted 11 Jan 2018cs.LG cs.AI stat.MLcited by 66

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Abstract (arXiv)

Current machine learning systems operate, almost exclusively, in a statistical, or model-free mode, which entails severe theoretical limits on their power and performance. Such systems cannot reason about interventions and retrospection and, therefore, cannot serve as the basis for strong AI. To achieve human level intelligence, learning machines need the guidance of a model of reality, similar to the ones used in causal inference tasks. To demonstrate the essential role of such models, I will present a summary of seven tasks which are beyond reach of current machine learning systems and which have been accomplished using the tools of causal modeling.

Key facts. Cited by 66 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Jan 2018.

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Citing papers per year: 2021: 11; 2022: 17; 2023: 15; 2024: 13; 2025: 5; 2026: 5.

66 papers.

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  1. 2022The Debate Over Understanding in AI's Large Language Modelscited by 142
  2. 2024Robust agents learn causal world modelscited by 81
  3. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  4. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  5. 2021Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use casescited by 39
  6. 2022Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Reviewcited by 37
  7. 2021Tell me why! Explanations support learning relational and causal structurecited by 36
  8. 2022Reinforced Causal Explainer for Graph Neural Networkscited by 34
  9. 2022Counterfactual harmcited by 29
  10. 2021Learning First-Order Representations for Planning from Black-Box States: New Resultscited by 23
  11. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  12. 2023Passive learning of active causal strategies in agents and language modelscited by 21
38 more
  1. 2022Distribution inference risks: Identifying and mitigating sources of leakagecited by 17
  2. 2023The role of causality in explainable artificial intelligencecited by 17
  3. 2021Invariant Language Modelingcited by 15
  4. 2022Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Reportcited by 14
  5. 2022FL Games: A Federated Learning Framework for Distribution Shiftscited by 14
  6. 2023Causal models in string diagramscited by 14
  7. 2025General agents contain world modelscited by 14
  8. 2024Local Causal Structure Learning in the Presence of Latent Variablescited by 11
  9. 2024Towards Compositional Interpretability for XAIcited by 10
  10. 2022Interpretation of Black Box NLP Models: A Surveycited by 9
  11. 2023Causalainer: Causal Explainer for Automatic Video Summarizationcited by 8
  12. 2021Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimationcited by 7
  13. 2023A Survey on Open-Set Image Recognitioncited by 7
  14. 2024Deep Learning With DAGscited by 7
  15. 2022Intelligent Computing: The Latest Advances, Challenges and Futurecited by 6
  16. 2023Conditional Modeling Based Automatic Video Summarizationcited by 6
  17. 2022Invariant Ancestry Searchcited by 5
  18. 2024DiConStruct: Causal Concept-based Explanations through Black-Box Distillationcited by 5
  19. 2024CreDes: Causal Reasoning Enhancement and Dual-End Searching for Solving Long-Range Reasoning Problems using LLMscited by 5
  20. 2021Co-evolutionary hybrid intelligencecited by 4
  21. 2022COIN: Counterfactual Image Generation for VQA Interpretationcited by 4
  22. 2023Knowledge Guided Representation Learning and Causal Structure Learning in Soil Sciencecited by 3
  23. 2021How do some Bayesian Network machine learned graphs compare to causal knowledge?cited by 1
  24. 2021Estimation of Bivariate Structural Causal Models by Variational Gaussian Process Regression Under Likelihoods Parametrised by Normalising Flowscited by 1
  25. 2022Causal Discovery from Sparse Time-Series Data Using Echo State Networkcited by 1
  26. 2022Trust in Language Grounding: a new AI challenge for human-robot teamscited by 1
  27. 2022Provable Robust Saliency-based Explanationscited by 1
  28. 2023Counterfactual Prediction Under Selective Confoundingcited by 1
  29. 2024Why Online Reinforcement Learning is Causalcited by 1
  30. 2024Personalized Video Summarization using Text-Based Queries and Conditional Modelingcited by 1
  31. 2024Fast Causal Discovery by Approximate Kernel-based Generalized Score Functions with Linear Computational Complexitycited by 1
  32. 2025Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variablescited by 1
  33. 2021Technology Readiness Levels for Machine Learning Systemscited by 0
  34. 2021Risk Measurement, Risk Entropy, and Autonomous Driving Risk Modelingcited by 0
  35. 2022FL Games: A federated learning framework for distribution shiftscited by 0
  36. 2022Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applicationscited by 0
  37. 2023Deep Learning from Parametrically Generated Virtual Buildings for Real-World Object Recognitioncited by 0
  38. 2023Robust Ranking Explanationscited by 0

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