mlsanityData to 7 Oct 2026

Causal Discovery via Bayesian Optimization

arXiv 2501.14997 ↗First posted 25 Jan 2025cs.LG stat.MLcited by 1

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Abstract (arXiv)

Existing score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome this, in this study, we propose DrBO (DAG recovery via Bayesian Optimization)-a novel DAG learning framework leveraging Bayesian optimization (BO) to find high-scoring DAGs. We show that, by sophisticatedly choosing the promising DAGs to explore, we can find higher-scoring ones much more efficiently. To address the scalability issues of conventional BO in DAG learning, we replace Gaussian Processes commonly employed in BO with dropout neural networks, trained in a continual manner, which allows for (i) flexibly modeling the DAG scores without overfitting, (ii) incorporation of uncertainty into the estimated scores, and (iii) scaling with the number of evaluations. As a result, DrBO is computationally efficient and can find the accurate DAG in fewer trials and less time than existing state-of-the-art methods. This is demonstrated through an extensive set of empirical evaluations on many challenging settings with both synthetic and real data. Our implementation is available at https://github.com/baosws/DrBO.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 44 of its 90 references that are in the map. First posted on arXiv 25 Jan 2025.

Builds on

44 of 90 references.

References · 44

  1. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,979
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2023A Comprehensive Survey of Continual Learning: Theory, Method and Applicationcited by 860
  4. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  5. 2017Progressive Neural Architecture Searchcited by 483
  6. 2019Scalable Global Optimization via Local Bayesian Optimizationcited by 359
  7. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  8. 2015Scalable Bayesian Optimization Using Deep Neural Networkscited by 284
  9. 2013Learning to Optimize Via Posterior Samplingcited by 253
  10. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
  11. 2019Gradient-Based Neural DAG Learningcited by 227
  12. 2014Scalable Variational Gaussian Process Classificationcited by 226
32 more
  1. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  2. 2019Learning Sparse Nonparametric DAGscited by 207
  3. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 186
  4. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  5. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 175
  6. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  7. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  8. 2019Causal Discovery with Reinforcement Learningcited by 145
  9. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  10. 2017Batched Large-scale Bayesian Optimization in High-dimensional Spacescited by 116
  11. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 108
  12. 2021Multi-Objective Bayesian Optimization over High-Dimensional Search Spacescited by 87
  13. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  14. 2022Recent Advances in Bayesian Optimizationcited by 74
  15. 2022Diffusion Models for Causal Discovery via Topological Orderingcited by 64
  16. 2013Learning Gaussian Networkscited by 62
  17. 2021gCastle: A Python Toolbox for Causal Discoverycited by 60
  18. 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
  19. 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
  20. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  21. 2023Scalable Causal Discovery with Score Matchingcited by 42
  22. 2020Causal Bayesian Optimizationcited by 39
  23. 2022Active Bayesian Causal Inferencecited by 34
  24. 2014Addendum on the scoring of Gaussian directed acyclic graphical modelscited by 32
  25. 2020On Low Rank Directed Acyclic Graphs and Causal Structure Learningcited by 23
  26. 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
  27. 2018Deep Active Learning with a Neural Architecture Searchcited by 17
  28. 2021Dynamic Causal Bayesian Optimizationcited by 17
  29. 2023Computably Continuous Reinforcement-Learning Objectives are PAC-learnablecited by 13
  30. 2023Constraint-Free Structure Learning with Smooth Acyclic Orientationscited by 10
  31. 2023Differentiable Bayesian Structure Learning with Acyclicity Assurancecited by 3
  32. 2024Reinforcement Learning for Causal Discovery without Acyclicity Constraintscited by 3

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Citing papers per year: 2026: 1.

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  1. 2026Arrow: A Foundation Model for Causal Discoverycited by 4