mlsanityData to 7 Oct 2026

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayes

arXiv 2211.02763 ↗First posted 4 Nov 2022cs.LG stat.MLcited by 30

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Abstract (arXiv)

Bayesian causal structure learning aims to learn a posterior distribution over directed acyclic graphs (DAGs), and the mechanisms that define the relationship between parent and child variables. By taking a Bayesian approach, it is possible to reason about the uncertainty of the causal model. The notion of modelling the uncertainty over models is particularly crucial for causal structure learning since the model could be unidentifiable when given only a finite amount of observational data. In this paper, we introduce a novel method to jointly learn the structure and mechanisms of the causal model using Variational Bayes, which we call Variational Bayes-DAG-GFlowNet (VBG). We extend the method of Bayesian causal structure learning using GFlowNets to learn not only the posterior distribution over the structure, but also the parameters of a linear-Gaussian model. Our results on simulated data suggest that VBG is competitive against several baselines in modelling the posterior over DAGs and mechanisms, while offering several advantages over existing methods, including the guarantee to sample acyclic graphs, and the flexibility to generalize to non-linear causal mechanisms.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). Builds on 25 of its 48 references that are in the map. First posted on arXiv 4 Nov 2022.

Builds on

25 of 48 references.

References · 25

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2021Flow Network based Generative Models for Non-Iterative Diverse Candidate Generationcited by 384
  3. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  4. 2022Trajectory balance: Improved credit assignment in GFlowNetscited by 244
  5. 2019Gradient-Based Neural DAG Learningcited by 227
  6. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  7. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  8. 2022Learning GFlowNets from partial episodes for improved convergence and stabilitycited by 129
  9. 2019Learning Neural Causal Models from Unknown Interventionscited by 113
  10. 2022Generative Flow Networks for Discrete Probabilistic Modelingcited by 113
  11. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  12. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
13 more
  1. 2022Learning to Induce Causal Structurecited by 68
  2. 2022Greedy Relaxations of the Sparsest Permutation Algorithmcited by 63
  3. 2013Learning Gaussian Networkscited by 62
  4. 2019ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discoverycited by 52
  5. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  6. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  7. 2021Learning Neural Causal Models with Active Interventionscited by 40
  8. 2022Active Bayesian Causal Inferencecited by 34
  9. 2013Data Analysis with Bayesian Networks: A Bootstrap Approachcited by 31
  10. 2013Being Bayesian about Network Structurecited by 17
  11. 2022Tractable Uncertainty for Structure Learningcited by 15
  12. 2013On the Equivalence of Causal Modelscited by 13
  13. 2022Learning Latent Structural Causal Modelscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 15; 2024: 9; 2025: 5; 2026: 1.

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  1. 2023A theory of continuous generative flow networkscited by 98
  2. 2023Better Training of GFlowNets with Local Credit and Incomplete Trajectoriescited by 83
  3. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  4. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  5. 2023GFlowNet-EM for learning compositional latent variable modelscited by 40
  6. 2023Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNetscited by 40
  7. 2024Discrete Probabilistic Inference as Control in Multi-path Environmentscited by 40
  8. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  9. 2023Stochastic Generative Flow Networkscited by 32
  10. 2023Distributional GFlowNets with Quantile Flowscited by 28
  11. 2024Adaptive teachers for amortized samplerscited by 23
  12. 2023Passive learning of active causal strategies in agents and language modelscited by 21
18 more
  1. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  2. 2024Graph Neural Flows for Unveiling Systemic Interactions Among Irregularly Sampled Time Seriescited by 14
  3. 2023Order-Preserving GFlowNetscited by 13
  4. 2023Delta-AI: Local objectives for amortized inference in sparse graphical modelscited by 7
  5. 2025Hybrid-Balance GFlowNet for Solving Vehicle Routing Problemscited by 7
  6. 2025Compositional Flows for 3D Molecule and Synthesis Pathway Co-designcited by 6
  7. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  8. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4
  9. 2023Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphscited by 3
  10. 2023Differentiable Bayesian Structure Learning with Acyclicity Assurancecited by 3
  11. 2024Multi-Agent Continuous Control with Generative Flow Networkscited by 3
  12. 2024Possible Principles for Aligned Structure Learning Agentscited by 3
  13. 2025gfnx: Fast and Scalable Library for Generative Flow Networks in JAXcited by 2
  14. 2024Enhance Eye Disease Detection using Learnable Probabilistic Discrete Latents in Machine Learning Architecturescited by 1
  15. 2024Bifurcated Generative Flow Networkscited by 1
  16. 2025Learning Causal Structure Distributions for Robust Planningcited by 1
  17. 2025Planning-Augmented Sampling with Early Guidance for High-Reward Discoverycited by 0
  18. 2026SVI-DAG: A Structured Variational Inference Approach to Bayesian Causal Discoverycited by 0