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Causal Structure Learning

arXiv 1706.09141 ↗First posted 28 Jun 2017stat.MEcited by 84

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Abstract (arXiv)

Graphical models can represent a multivariate distribution in a convenient and accessible form as a graph. Causal models can be viewed as a special class of graphical models that not only represent the distribution of the observed system but also the distributions under external interventions. They hence enable predictions under hypothetical interventions, which is important for decision making. The challenging task of learning causal models from data always relies on some underlying assumptions. We discuss several recently proposed structure learning algorithms and their assumptions, and compare their empirical performance under various scenarios.

Key facts. Cited by 84 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 19; 2023: 18; 2024: 13; 2025: 11; 2026: 9.

84 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2022Amortized Inference for Causal Structure Learningcited by 89
  3. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  4. 2021Desiderata for Representation Learning: A Causal Perspectivecited by 76
  5. 2022Learning to Induce Causal Structurecited by 68
  6. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  7. 2023Understanding Causality with Large Language Models: Feasibility and Opportunitiescited by 53
  8. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  9. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  10. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  11. 2021Learning Neural Causal Models with Active Interventionscited by 40
  12. 2021Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspectivecited by 40
38 more
  1. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  2. 2022Active Bayesian Causal Inferencecited by 34
  3. 2023Discovering Causal Relations and Equations from Datacited by 33
  4. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  5. 2023Assumption violations in causal discovery and the robustness of score matchingcited by 28
  6. 2024MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal Assistantscited by 24
  7. 2022Improvement-Focused Causal Recourse (ICR)cited by 20
  8. 2022Deep Learning of Causal Structures in High Dimensionscited by 20
  9. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  10. 2023The role of causality in explainable artificial intelligencecited by 17
  11. 2024Adjustment Identification Distance: A gadjid for Causal Structure Learningcited by 16
  12. 2021Beyond Predictions in Neural ODEs: Identification and Interventionscited by 15
  13. 2022Distributed Learning of Generalized Linear Causal Networkscited by 13
  14. 2023DRCFS: Doubly Robust Causal Feature Selectioncited by 12
  15. 2024The Causal Chambers: Real Physical Systems as a Testbed for AI Methodologycited by 12
  16. 2022Deep Causal Learning: Representation, Discovery and Inferencecited by 11
  17. 2024Local Causal Structure Learning in the Presence of Latent Variablescited by 11
  18. 2021Typing assumptions improve identification in causal discoverycited by 10
  19. 2022Valid Inference After Causal Discoverycited by 10
  20. 2023Constraint-Free Structure Learning with Smooth Acyclic Orientationscited by 10
  21. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
  22. 2022Methods for Recovering Conditional Independence Graphs: A Surveycited by 7
  23. 2022Quantitative probing: Validating causal models using quantitative domain knowledgecited by 6
  24. 2023MetaPhysiCa: OOD Robustness in Physics-informed Machine Learningcited by 6
  25. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  26. 2024Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithmcited by 5
  27. 2021Shadow-Mapping for Unsupervised Neural Causal Discoverycited by 4
  28. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  29. 2021Partially Observed Exchangeable Modelingcited by 3
  30. 2022Causality, Causal Discovery, and Causal Inference in Structural Engineeringcited by 3
  31. 2023DiscoGen: Learning to Discover Gene Regulatory Networkscited by 3
  32. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  33. 2025Dynamic Causal Structure Discovery and Causal Effect Estimationcited by 3
  34. 2021Multi-Agent Algorithmic Recoursecited by 2
  35. 2022Trying to Outrun Causality with Machine Learning: Limitations of Model Explainability Techniques for Identifying Predictive Variablescited by 2
  36. 2022CKH: Causal Knowledge Hierarchy for Estimating Structural Causal Models from Data and Priorscited by 2
  37. 2022A Causal Research Pipeline and Tutorial for Psychologists and Social Scientistscited by 2
  38. 2022Improving Data-driven Heterogeneous Treatment Effect Estimation Under Structure Uncertaintycited by 2

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