mlsanityData to 7 Oct 2026

DiBS: Differentiable Bayesian Structure Learning

arXiv 2105.11839 ↗First posted 25 May 2021cs.LG stat.MLcited by 105

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Abstract (arXiv)

Bayesian structure learning allows inferring Bayesian network structure from data while reasoning about the epistemic uncertainty -- a key element towards enabling active causal discovery and designing interventions in real world systems. In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation. Contrary to existing work, DiBS is agnostic to the form of the local conditional distributions and allows for joint posterior inference of both the graph structure and the conditional distribution parameters. This makes our formulation directly applicable to posterior inference of complex Bayesian network models, e.g., with nonlinear dependencies encoded by neural networks. Using DiBS, we devise an efficient, general purpose variational inference method for approximating distributions over structural models. In evaluations on simulated and real-world data, our method significantly outperforms related approaches to joint posterior inference.

Key facts. Cited by 105 papers in the map (data through 7 Oct 2026). Builds on 29 of its 74 references that are in the map. First posted on arXiv 25 May 2021.

Builds on

29 of 74 references.

References · 29

  1. 2016Variational Graph Auto-Encoderscited by 1,373
  2. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  3. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  4. 2016Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithmcited by 498
  5. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  6. 2019Monte Carlo Gradient Estimation in Machine Learningcited by 281
  7. 2019Gradient-Based Neural DAG Learningcited by 227
  8. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  9. 2019Learning Sparse Nonparametric DAGscited by 207
  10. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
  11. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 186
  12. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
17 more
  1. 2019A Meta-Transfer Objective for Learning to Disentangle Causal Mechanismscited by 158
  2. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  3. 2019D-VAE: A Variational Autoencoder for Directed Acyclic Graphscited by 98
  4. 2019Masked Gradient-Based Causal Structure Learningcited by 89
  5. 2020DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networkscited by 66
  6. 2013Learning Gaussian Networkscited by 62
  7. 2019A Graph Autoencoder Approach to Causal Structure Learningcited by 55
  8. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  9. 2019Causal Discovery Toolbox: Uncover causal relationships in Pythoncited by 49
  10. 2018Structural Agnostic Modeling: Adversarial Learning of Causal Graphscited by 48
  11. 2013Parameter Priors for Directed Acyclic Graphical Models and the Characterization of Several Probability Distributionscited by 41
  12. 2020On the Convergence of Continuous Constrained Optimization for Structure Learningcited by 38
  13. 2015Partition MCMC for inference on acyclic digraphscited by 33
  14. 2014Addendum on the scoring of Gaussian directed acyclic graphical modelscited by 32
  15. 2013Data Analysis with Bayesian Networks: A Bootstrap Approachcited by 31
  16. 2013Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphscited by 25
  17. 2019Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networkscited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 18; 2023: 22; 2024: 23; 2025: 13; 2026: 24.

105 papers.

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  1. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  2. 2023A theory of continuous generative flow networkscited by 98
  3. 2022Deep End-to-end Causal Inferencecited by 93
  4. 2022Amortized Inference for Causal Structure Learningcited by 89
  5. 2023Identifiability Guarantees for Causal Disentanglement from Soft Interventionscited by 84
  6. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  7. 2022On the Identifiability and Estimation of Causal Location-Scale Noise Modelscited by 56
  8. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  9. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  10. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  11. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  12. 2021Learning Neural Causal Models with Active Interventionscited by 40
38 more
  1. 2022Active Bayesian Causal Inferencecited by 34
  2. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  3. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  4. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
  5. 2023DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNetscited by 27
  6. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  7. 2024A Meta-Learning Approach to Bayesian Causal Discoverycited by 22
  8. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  9. 2024Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processescited by 18
  10. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  11. 2022Tractable Uncertainty for Structure Learningcited by 15
  12. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  13. 2023Benchmarking and Explaining Large Language Model-based Code Generation: A Causality-Centric Approachcited by 15
  14. 2023Computably Continuous Reinforcement-Learning Objectives are PAC-learnablecited by 13
  15. 2024Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environmentcited by 12
  16. 2021ML4C: Seeing Causality Through Latent Vicinitycited by 11
  17. 2023Toward Falsifying Causal Graphs Using a Permutation-Based Testcited by 11
  18. 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learningcited by 11
  19. 2023Discovering Mixtures of Structural Causal Models from Time Series Datacited by 10
  20. 2022On the Generalization and Adaption Performance of Causal Modelscited by 8
  21. 2023Causality-Aided Trade-off Analysis for Machine Learning Fairnesscited by 6
  22. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  23. 2024Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)cited by 6
  24. 2022Learning Latent Structural Causal Modelscited by 5
  25. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  26. 2023Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systemscited by 5
  27. 2024Optimal Transport for Structure Learning Under Missing Datacited by 5
  28. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  29. 2024Continuous Bayesian Model Selection for Multivariate Causal Discoverycited by 5
  30. 2023A Bayesian Take on Gaussian Process Networkscited by 4
  31. 2023Structure Learning with Adaptive Random Neighborhood Informed MCMCcited by 4
  32. 2024Permutation-based Inference for Variational Learning of Directed Acyclic Graphscited by 4
  33. 2024ProDAG: Projected Variational Inference for Directed Acyclic Graphscited by 4
  34. 2024Amortized Inference of Causal Models via Conditional Fixed-Point Iterationscited by 4
  35. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4
  36. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
  37. 2025Since Faithfulness Fails: The Performance Limits of Neural Causal Discoverycited by 4
  38. 2026Arrow: A Foundation Model for Causal Discoverycited by 4

50 most cited of 105 All 105 (JSON)