mlsanityData to 14 Oct 2026

D'ya like DAGs? A Survey on Structure Learning and Causal Discovery

arXiv 2103.02582 ↗First posted 3 Mar 2021cs.LG stat.ME stat.MLcited by 211

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Abstract (arXiv)

Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. We provide a review of background theory and a survey of methods for structure discovery. We primarily focus on modern, continuous optimization methods, and provide reference to further resources such as benchmark datasets and software packages. Finally, we discuss the assumptive leap required to take us from structure to causality.

Key facts. Cited by 211 papers in the map (data through 14 Oct 2026). Builds on 114 of its 293 references that are in the map. First posted on arXiv 3 Mar 2021.

Builds on

114 of 293 references.

References · 114

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38 more
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50 most cited of 114 All 114 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 11; 2022: 39; 2023: 49; 2024: 47; 2025: 35; 2026: 30.

211 papers.

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  1. 2024Language Agents as Optimizable Graphscited by 89
  2. 2021A survey of Bayesian Network structure learningcited by 87
  3. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  4. 2024Robust agents learn causal world modelscited by 81
  5. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  6. 2023A Survey on Causal Discovery: Theory and Practicecited by 63
  7. 2024Are LLMs Capable of Data-based Statistical and Causal Reasoning? Benchmarking Advanced Quantitative Reasoning with Datacited by 63
  8. 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
  9. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  10. 2021Unsuitability of NOTEARS for Causal Graph Discoverycited by 47
  11. 2022Differentiable DAG Samplingcited by 47
  12. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
38 more
  1. 2023CUTS+: High-dimensional Causal Discovery from Irregular Time-seriescited by 41
  2. 2021Learning Neural Causal Models with Active Interventionscited by 40
  3. 2021Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspectivecited by 40
  4. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 36
  5. 2023CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discoverycited by 36
  6. 2022Active Bayesian Causal Inferencecited by 35
  7. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  8. 2023CUTS: Neural Causal Discovery from Irregular Time-Series Datacited by 33
  9. 2024Causal Inference with Large Language Model: A Surveycited by 32
  10. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  11. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
  12. 2023Causal Reinforcement Learning: A Surveycited by 23
  13. 2023From Identifiable Causal Representations to Controllable Counterfactual Generation: A Survey on Causal Generative Modelingcited by 22
  14. 2022Causal Balancing for Domain Generalizationcited by 20
  15. 2021VACA: Design of Variational Graph Autoencoders for Interventional and Counterfactual Queriescited by 19
  16. 2022Data-Driven Causal Effect Estimation Based on Graphical Causal Modelling: A Surveycited by 19
  17. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  18. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  19. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  20. 2024Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic Modelscited by 18
  21. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 18
  22. 2024Standardizing Structural Causal Modelscited by 17
  23. 2024CausalFormer: An Interpretable Transformer for Temporal Causal Discoverycited by 17
  24. 2025Causally Reliable Concept Bottleneck Modelscited by 17
  25. 2021Beyond Predictions in Neural ODEs: Identification and Interventionscited by 15
  26. 2023On Learning Necessary and Sufficient Causal Graphscited by 14
  27. 2023Learning Causally Disentangled Representations via the Principle of Independent Causal Mechanismscited by 14
  28. 2022Why did I fail? A Causal-based Method to Find Explanations for Robot Failurescited by 13
  29. 2022Causal Entropy Optimizationcited by 13
  30. 2022Sequential Learning Of Neural Networks for Prequential MDLcited by 13
  31. 2023RDGSL: Dynamic Graph Representation Learning with Structure Learningcited by 13
  32. 2023CoLiDE: Concomitant Linear DAG Estimationcited by 13
  33. 2022Structural Causal 3D Reconstructioncited by 12
  34. 2022Causal Fourier Analysis on Directed Acyclic Graphs and Posetscited by 12
  35. 2022Applications of statistical causal inference in software engineeringcited by 12
  36. 2023Dynamic Causal Graph Convolutional Network for Traffic Predictioncited by 12
  37. 2023Through the Lens of Core Competency: Survey on Evaluation of Large Language Modelscited by 12
  38. 2024Recursive Causal Discoverycited by 12

50 most cited of 211 All 211 (JSON)