mlsanityData to 7 Oct 2026

Towards Scalable Bayesian Learning of Causal DAGs

arXiv 2010.00684 ↗First posted 30 Sep 2020cs.LG cs.AIcited by 33

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Abstract (arXiv)

We give methods for Bayesian inference of directed acyclic graphs, DAGs, and the induced causal effects from passively observed complete data. Our methods build on a recent Markov chain Monte Carlo scheme for learning Bayesian networks, which enables efficient approximate sampling from the graph posterior, provided that each node is assigned a small number K of candidate parents. We present algorithmic techniques to significantly reduce the space and time requirements, which make the use of substantially larger values of K feasible. Furthermore, we investigate the problem of selecting the candidate parents per node so as to maximize the covered posterior mass. Finally, we combine our sampling method with a novel Bayesian approach for estimating causal effects in linear Gaussian DAG models. Numerical experiments demonstrate the performance of our methods in detecting ancestor-descendant relations, and in causal effect estimation our Bayesian method is shown to outperform previous approaches.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Sep 2020.

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Citing papers per year: 2021: 7; 2022: 7; 2023: 7; 2024: 5; 2025: 2; 2026: 5.

33 papers.

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  1. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  2. 2022Deep End-to-end Causal Inferencecited by 93
  3. 2021A survey of Bayesian Network structure learningcited by 85
  4. 2021BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycited by 68
  5. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  6. 2023Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Networkcited by 50
  7. 2022Differentiable DAG Samplingcited by 46
  8. 2021Simultaneous Missing Value Imputation and Structure Learning with Groupscited by 16
  9. 2022Tractable Uncertainty for Structure Learningcited by 15
  10. 2023CoLiDE: Concomitant Linear DAG Estimationcited by 12
  11. 2025Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learningcited by 11
  12. 2021Context-Specific Causal Discovery for Categorical Data Using Staged Treescited by 10
21 more
  1. 2021Interactive Causal Structure Discovery in Earth System Sciencescited by 6
  2. 2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
  3. 2024Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)cited by 6
  4. 2022Learning Latent Structural Causal Modelscited by 5
  5. 2022The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventionscited by 4
  6. 2023A Bayesian Take on Gaussian Process Networkscited by 4
  7. 2024ProDAG: Projected Variational Inference for Directed Acyclic Graphscited by 4
  8. 2023CMVAE: Causal Meta VAE for Unsupervised Meta-Learningcited by 3
  9. 2023Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimationcited by 3
  10. 2021Scalable Bayesian Network Structure Learning with Splinescited by 2
  11. 2021The Dual PC Algorithm and the Role of Gaussianity for Structure Learning of Bayesian Networkscited by 2
  12. 2024Bayesian Causal Inference with Gaussian Process Networkscited by 2
  13. 2026Concomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativitycited by 2
  14. 2022Latent Variable Models for Bayesian Causal Discoverycited by 1
  15. 2024Scalable Variational Causal Discovery Unconstrained by Acyclicitycited by 1
  16. 2023Bayesian Approach to Linear Bayesian Networkscited by 0
  17. 2025Scaling Up Bayesian DAG Samplingcited by 0
  18. 2026A Topological Sorting Criterion for Random Causal Directed Acyclic Graphscited by 0
  19. 2026Structured Inference with Large Language Gibbscited by 0
  20. 2026How Does Bayesian Causal Discovery Fail? Characterising Structural Consequences in Linear Gaussian Networks under Latent Confoundingcited by 0
  21. 2026Interpretable Causal Discovery via Causal-Effect Constraintscited by 0