mlsanityData to 7 Oct 2026

ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery

arXiv 1902.10347 ↗First posted 27 Feb 2019stat.MEcited by 52

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Abstract (arXiv)

Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited interventional data. Given that randomized experiments are usually expensive to perform, we propose a general framework and theory based on optimal Bayesian experimental design to select experiments for targeted causal discovery. That is, we assume the experimenter is interested in learning some function of the unknown graph (e.g., all descendants of a target node) subject to design constraints such as limits on the number of samples and rounds of experimentation. While it is in general computationally intractable to select an optimal experimental design strategy, we provide a tractable implementation with provable guarantees on both approximation and optimization quality based on submodularity. We evaluate the efficacy of our proposed method on both synthetic and real datasets, thereby demonstrating that our method realizes considerable performance gains over baseline strategies such as random sampling.

Key facts. Cited by 52 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Feb 2019.

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Citing papers per year: 2021: 8; 2022: 16; 2023: 6; 2024: 10; 2025: 6; 2026: 6.

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  1. 2021DiBS: Differentiable Bayesian Structure Learningcited by 105
  2. 2023GFlowNets for AI-Driven Scientific Discoverycited by 63
  3. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  4. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  5. 2021Variational Causal Networks: Approximate Bayesian Inference over Causal Structurescited by 43
  6. 2021Learning Neural Causal Models with Active Interventionscited by 40
  7. 2022Active Bayesian Causal Inferencecited by 34
  8. 2023BayesDAG: Gradient-Based Posterior Inference for Causal Discoverycited by 33
  9. 2022Active Learning for Optimal Intervention Design in Causal Modelscited by 30
  10. 2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
  11. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  12. 2024A Meta-Learning Approach to Bayesian Causal Discoverycited by 22
38 more
  1. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  2. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  3. 2024The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applicationscited by 17
  4. 2021Matching a Desired Causal State via Shift Interventionscited by 16
  5. 2022Tractable Uncertainty for Structure Learningcited by 15
  6. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  7. 2022Causal Entropy Optimizationcited by 13
  8. 2023Active causal structure learning with advicecited by 10
  9. 2024Amortized Active Causal Induction with Deep Reinforcement Learningcited by 10
  10. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  11. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
  12. 2022Learning Latent Structural Causal Modelscited by 5
  13. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  14. 2023Statistically Efficient Bayesian Sequential Experiment Design via Reinforcement Learning with Cross-Entropy Estimatorscited by 5
  15. 2024Challenges and Considerations in the Evaluation of Bayesian Causal Discoverycited by 5
  16. 2024Targeted Sequential Indirect Experiment Designcited by 4
  17. 2024Generative Intervention Models for Causal Perturbation Modelingcited by 4
  18. 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelscited by 4
  19. 2022Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networkscited by 3
  20. 2023Meek Separators and Their Applications in Targeted Causal Discoverycited by 3
  21. 2025Causal Structure and Representation Learning with Biomedical Applicationscited by 3
  22. 2021Universal Lower Bound for Learning Causal DAGs with Atomic Interventionscited by 2
  23. 2022Automated causal inference in application to randomized controlled clinical trialscited by 2
  24. 2024Causal Discovery under Off-Target Interventionscited by 2
  25. 2021Randomized Algorithms for Monotone Submodular Function Maximization on the Integer Latticecited by 1
  26. 2022Experimental Design for Causal Effect Identificationcited by 1
  27. 2024Scalable Variational Causal Discovery Unconstrained by Acyclicitycited by 1
  28. 2024Graph Agnostic Causal Bayesian Optimisationcited by 1
  29. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 1
  30. 2026Why LLMs Fail at Causal Discovery and How Interventional Agents Escapecited by 1
  31. 2022Probability trees and the value of a single interventioncited by 0
  32. 2022Fast Causal Orientation Learning in Directed Acyclic Graphscited by 0
  33. 2024Bayesian Intervention Optimization for Causal Discoverycited by 0
  34. 2025Design of Experiment for Discovering Directed Mixed Graphcited by 0
  35. 2025Theoretical Guarantees for Causal Discovery on Large Random Graphscited by 0
  36. 2026Moment Matters: Mean and Variance Causal Graph Discovery from Heteroscedastic Observational Datacited by 0
  37. 2026Discrete Causal Representations from Heterogeneous Domains: A Bayesian Approach with Social Survey Applicationscited by 0
  38. 2026Adversarial Causal Intervention Falsificationcited by 0

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