mlsanityData to 7 Oct 2026

Budgeted Experiment Design for Causal Structure Learning

arXiv 1709.03625 ↗First posted 11 Sep 2017cs.LG cs.AI stat.MLcited by 38

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Abstract (arXiv)

We study the problem of causal structure learning when the experimenter is limited to perform at most k non-adaptive experiments of size 1. We formulate the problem of finding the best intervention target set as an optimization problem, which aims to maximize the average number of edges whose directions are resolved. We prove that the corresponding objective function is submodular and a greedy algorithm suffices to achieve (1-frac1e)-approximation of the optimal value. We further present an accelerated variant of the greedy algorithm, which can lead to orders of magnitude performance speedup. We validate our proposed approach on synthetic and real graphs. The results show that compared to the purely observational setting, our algorithm orients the majority of the edges through a considerably small number of interventions.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Sep 2017.

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Citing papers per year: 2021: 7; 2022: 12; 2023: 4; 2024: 4; 2025: 7; 2026: 4.

38 papers.

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  1. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  2. 2021Learning Neural Causal Models with Active Interventionscited by 40
  3. 2022Active Bayesian Causal Inferencecited by 34
  4. 2021GeneDisco: A Benchmark for Experimental Design in Drug Discoverycited by 18
  5. 2022Verification and search algorithms for causal DAGscited by 18
  6. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  7. 2021Matching a Desired Causal State via Shift Interventionscited by 16
  8. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  9. 2022Causal Entropy Optimizationcited by 13
  10. 2022Provably Efficient Causal Model-Based Reinforcement Learning for Systematic Generalizationcited by 12
  11. 2023Subset verification and search algorithms for causal DAGscited by 12
  12. 2022Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applicationscited by 9
26 more
  1. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  2. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
  3. 2022Sample Constrained Treatment Effect Estimationcited by 7
  4. 2022Neural Design for Genetic Perturbation Experimentscited by 6
  5. 2021Collaborative Causal Discovery with Atomic Interventionscited by 5
  6. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  7. 2024Causality from Bottom to Top: A Surveycited by 5
  8. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4
  9. 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelscited by 4
  10. 2023New metrics and search algorithms for weighted causal DAGscited by 3
  11. 2023Meek Separators and Their Applications in Targeted Causal Discoverycited by 3
  12. 2021Universal Lower Bound for Learning Causal DAGs with Atomic Interventionscited by 2
  13. 2024Causal Discovery under Off-Target Interventionscited by 2
  14. 2025Characterization and Learning of Causal Graphs from Hard Interventionscited by 2
  15. 2021Disentangling Observed Causal Effects from Latent Confounders using Method of Momentscited by 1
  16. 2022Experimental Design for Causal Effect Identificationcited by 1
  17. 2024Graph Agnostic Causal Bayesian Optimisationcited by 1
  18. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 1
  19. 2022Fast Causal Orientation Learning in Directed Acyclic Graphscited by 0
  20. 2025Causal Policy Learning in Reinforcement Learning: Backdoor-Adjusted Soft Actor-Criticcited by 0
  21. 2025Design of Experiment for Discovering Directed Mixed Graphcited by 0
  22. 2025Theoretical Guarantees for Causal Discovery on Large Random Graphscited by 0
  23. 2026Scalable Contrastive Causal Discovery under Unknown Soft Interventionscited by 0
  24. 2026Towards a holistic understanding of Selection Bias for Causal Effect Identificationcited by 0
  25. 2026FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventionscited by 0
  26. 2026Certifying Interventional Agreement Among Observationally Equivalent Causal Modelscited by 0