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Causal Preference Elicitation

arXiv 2602.01483 ↗First posted 1 Feb 2026cs.LG cs.AI stat.MEcited by 0

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Abstract (arXiv)

We propose causal preference elicitation, a Bayesian framework for expert-in-the-loop causal discovery that actively queries local edge relations to concentrate a posterior over directed acyclic graphs (DAGs). From any black-box observational posterior, we model noisy expert judgments with a three-way likelihood over edge existence and direction. Posterior inference uses a flexible particle approximation, and queries are selected by an efficient expected information gain criterion on the expert's categorical response. Experiments on synthetic graphs, protein signaling data, and a human gene perturbation benchmark show faster posterior concentration and improved recovery of directed effects under tight query budgets.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 57 references that are in the map. First posted on arXiv 1 Feb 2026.

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References · 24

  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. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  4. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 168
  5. 2022Bayesian Structure Learning with Generative Flow Networkscited by 153
  6. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  7. 2021A survey of Bayesian Network structure learningcited by 85
  8. 2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Treescited by 38
  9. 2022Active Bayesian Causal Inferencecited by 34
  10. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  11. 2018Efficient Sampling and Structure Learning of Bayesian Networkscited by 28
  12. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
12 more
  1. 2015Learning Structures of Bayesian Networks for Variable Groupscited by 11
  2. 2021Typing assumptions improve identification in causal discoverycited by 10
  3. 2023Active causal structure learning with advicecited by 10
  4. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
  5. 2021On the Sample Complexity of Causal Discovery and the Value of Domain Expertisecited by 9
  6. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  7. 2023Causal discovery using dynamically requested knowledgecited by 5
  8. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  9. 2024COKE: Causal Discovery with Chronological Order and Expert Knowledge in High Proportion of Missing Manufacturing Datacited by 5
  10. 2021Active Structure Learning of Bayesian Networks in an Observational Settingcited by 4
  11. 2024Permutation-based Inference for Variational Learning of Directed Acyclic Graphscited by 4
  12. 2024Learning Directed Acyclic Graphs from Partial Orderingscited by 2

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