mlsanityData to 14 Oct 2026

Experimental Design for Cost-Aware Learning of Causal Graphs

arXiv 1810.11867 ↗First posted 28 Oct 2018cs.LG cs.DM stat.MLcited by 25

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Abstract (arXiv)

We consider the minimum cost intervention design problem: Given the essential graph of a causal graph and a cost to intervene on a variable, identify the set of interventions with minimum total cost that can learn any causal graph with the given essential graph. We first show that this problem is NP-hard. We then prove that we can achieve a constant factor approximation to this problem with a greedy algorithm. We then constrain the sparsity of each intervention. We develop an algorithm that returns an intervention design that is nearly optimal in terms of size for sparse graphs with sparse interventions and we discuss how to use it when there are costs on the vertices.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Oct 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 5; 2022: 8; 2023: 4; 2024: 5; 2025: 1; 2026: 2.

25 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. 2021Causal Bandits with Unknown Graph Structurecited by 37
  4. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 36
  5. 2022Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoningcited by 34
  6. 2022Adaptively Exploiting d-Separators with Causal Banditscited by 18
  7. 2022Verification and search algorithms for causal DAGscited by 18
  8. 2021Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learningcited by 17
  9. 2023Differentiable Multi-Target Causal Bayesian Experimental Designcited by 15
  10. 2023Subset verification and search algorithms for causal DAGscited by 12
  11. 2023Active causal structure learning with advicecited by 10
  12. 2022A Unified Experiment Design Approach for Cyclic and Acyclic Causal Modelscited by 7
13 more
  1. 2021Collaborative Causal Discovery with Atomic Interventionscited by 5
  2. 2022Trust Your nabla: Gradient-based Intervention Targeting for Causal Discoverycited by 5
  3. 2024Confounded Budgeted Causal Banditscited by 4
  4. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4
  5. 2023New metrics and search algorithms for weighted causal DAGscited by 3
  6. 2024Interventional Causal Discovery in a Mixture of DAGscited by 3
  7. 2021Active Learning of Continuous-time Bayesian Networks through Interventionscited by 2
  8. 2022Experimental Design for Causal Effect Identificationcited by 2
  9. 2024Causal Discovery under Off-Target Interventionscited by 2
  10. 2024Bayesian Intervention Optimization for Causal Discoverycited by 0
  11. 2025From Observation to Orientation: an Adaptive Integer Programming Approach to Intervention Designcited by 0
  12. 2026FOCUS: Fixed-Confidence Online Causal Learning Using Sequential Adaptive Interventionscited by 0
  13. 2026Certifying Interventional Agreement Among Observationally Equivalent Causal Modelscited by 0