mlsanityData to 7 Oct 2026

Cost-Optimal Learning of Causal Graphs

arXiv 1703.02645 ↗First posted 8 Mar 2017cs.AI cs.IT math.ITcited by 44

Not on the map. Why?

Abstract (arXiv)

We consider the problem of learning a causal graph over a set of variables with interventions. We study the cost-optimal causal graph learning problem: For a given skeleton (undirected version of the causal graph), design the set of interventions with minimum total cost, that can uniquely identify any causal graph with the given skeleton. We show that this problem is solvable in polynomial time. Later, we consider the case when the number of interventions is limited. For this case, we provide polynomial time algorithms when the skeleton is a tree or a clique tree. For a general chordal skeleton, we develop an efficient greedy algorithm, which can be improved when the causal graph skeleton is an interval graph.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 9; 2023: 8; 2024: 8; 2025: 9; 2026: 4.

44 papers.

Sort:

  1. 2022Interventions, Where and How? Experimental Design for Causal Models at Scalecited by 49
  2. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  3. 2021Learning Neural Causal Models with Active Interventionscited by 40
  4. 2021Causal Bandits with Unknown Graph Structurecited by 37
  5. 2022Evaluation Methods and Measures for Causal Learning Algorithmscited by 23
  6. 2022Adaptively Exploiting d-Separators with Causal Banditscited by 18
  7. 2022Verification and search algorithms for causal DAGscited by 18
  8. 2023Identifying Linearly-Mixed Causal Representations from Multi-Node Interventionscited by 18
  9. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  10. 2021Matching a Desired Causal State via Shift Interventionscited by 16
  11. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  12. 2022Causal Entropy Optimizationcited by 13
32 more
  1. 2023Subset verification and search algorithms for causal DAGscited by 12
  2. 2023Active causal structure learning with advicecited by 10
  3. 2023DAG Learning on the Permutahedroncited by 9
  4. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 9
  5. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
  6. 2022Sample Constrained Treatment Effect Estimationcited by 7
  7. 2023Adaptivity Complexity for Causal Graph Discoverycited by 7
  8. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 6
  9. 2024Adaptive Online Experimental Design for Causal Discoverycited by 5
  10. 2024Confounded Budgeted Causal Banditscited by 4
  11. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4
  12. 2025Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelscited by 4
  13. 2023New metrics and search algorithms for weighted causal DAGscited by 3
  14. 2023Meek Separators and Their Applications in Targeted Causal Discoverycited by 3
  15. 2024Interventional Causal Discovery in a Mixture of DAGscited by 3
  16. 2021Universal Lower Bound for Learning Causal DAGs with Atomic Interventionscited by 2
  17. 2024Causal Discovery under Off-Target Interventionscited by 2
  18. 2025Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Datacited by 2
  19. 2021Intervention Efficient Algorithm for Two-Stage Causal MDPscited by 1
  20. 2024Causal Contextual Bandits with Adaptive Contextcited by 1
  21. 2024Graph Agnostic Causal Bayesian Optimisationcited by 1
  22. 2025Goal-Oriented Sequential Bayesian Experimental Design for Causal Learningcited by 1
  23. 2022Fast Causal Orientation Learning in Directed Acyclic Graphscited by 0
  24. 2024Bayesian Intervention Optimization for Causal Discoverycited by 0
  25. 2025Sample Complexity of Nonparametric Closeness Testing for Continuous Distributions and Its Application to Causal Discovery with Hidden Confoundingcited by 0
  26. 2025From Observation to Orientation: an Adaptive Integer Programming Approach to Intervention Designcited by 0
  27. 2025Design of Experiment for Discovering Directed Mixed Graphcited by 0
  28. 2025Theoretical Guarantees for Causal Discovery on Large Random Graphscited by 0
  29. 2026Non-Parametric Rehearsal Learning via Conditional Mean Embeddingscited by 0
  30. 2026Towards a holistic understanding of Selection Bias for Causal Effect Identificationcited by 0
  31. 2026FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventionscited by 0
  32. 2026Certifying Interventional Agreement Among Observationally Equivalent Causal Modelscited by 0