mlsanityData to 7 Oct 2026

Gradient-Based Neural DAG Learning

arXiv 1906.02226 ↗First posted 5 Jun 2019cs.LG stat.MLcited by 227

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Abstract (arXiv)

We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model complex interactions while avoiding the combinatorial nature of the problem. In addition to comparing our method to existing continuous optimization methods, we provide missing empirical comparisons to nonlinear greedy search methods. On both synthetic and real-world data sets, this new method outperforms current continuous methods on most tasks, while being competitive with existing greedy search methods on important metrics for causal inference.

Key facts. Cited by 227 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 24; 2022: 39; 2023: 55; 2024: 39; 2025: 33; 2026: 37.

227 papers.

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  1. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 209
  2. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 169
  3. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  4. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  5. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 108
  6. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  7. 2022Deep End-to-end Causal Inferencecited by 93
  8. 2022Amortized Inference for Causal Structure Learningcited by 89
  9. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  10. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  11. 2022Learning to Induce Causal Structurecited by 68
  12. 2022Diffusion Models for Causal Discovery via Topological Orderingcited by 64
38 more
  1. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  2. 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
  3. 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
  4. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  5. 2022Differentiable DAG Samplingcited by 46
  6. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  7. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  8. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  9. 2023Scalable Causal Discovery with Score Matchingcited by 42
  10. 2023Causal Discovery with Score Matching on Additive Models with Arbitrary Noisecited by 41
  11. 2021Learning Neural Causal Models with Active Interventionscited by 40
  12. 2023A Survey on Causal Discovery Methods for I.I.D. and Time Series Datacited by 35
  13. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 34
  14. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  15. 2023Discovering Causal Relations and Equations from Datacited by 33
  16. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  17. 2021Towards Federated Bayesian Network Structure Learning with Continuous Optimizationcited by 31
  18. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
  19. 2023Stable Differentiable Causal Discoverycited by 30
  20. 2022Improving Multi-Task Generalization via Regularizing Spurious Correlationcited by 28
  21. 2023Assumption violations in causal discovery and the robustness of score matchingcited by 28
  22. 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetscited by 27
  23. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  24. 2021Simultaneously Reconciled Quantile Forecasting of Hierarchically Related Time Seriescited by 24
  25. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
  26. 2022Causality-driven Hierarchical Structure Discovery for Reinforcement Learningcited by 23
  27. 2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
  28. 2024A Meta-Learning Approach to Bayesian Causal Discoverycited by 22
  29. 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
  30. 2022MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Modelscited by 21
  31. 2024Position: Understanding LLMs Requires More Than Statistical Generalizationcited by 20
  32. 2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 19
  33. 2023Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systemscited by 18
  34. 2023textttcausalAssembly: Generating Realistic Production Data for Benchmarking Causal Discoverycited by 18
  35. 2021Structured Sparsity Inducing Adaptive Optimizers for Deep Learningcited by 17
  36. 2021FedDAG: Federated DAG Structure Learningcited by 17
  37. 2023Optimizing NOTEARS Objectives via Topological Swapscited by 17
  38. 2023Towards Causal Foundation Model: on Duality between Causal Inference and Attentioncited by 17

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