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Efficient Causal Structure Learning via Modular Subgraph Integration

arXiv 2601.21014 ↗First posted 28 Jan 2026stat.ML cs.LG stat.APcited by 0

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Abstract (arXiv)

Learning causal structures from observational data remains a fundamental yet computationally intensive task, particularly in high-dimensional settings where existing methods face challenges such as the super-exponential growth of the search space and increasing computational demands. To address this, we introduce VISTA (Voting-based Integration of Subgraph Topologies for Acyclicity), a modular framework that decomposes the global causal structure learning problem into local subgraphs based on Markov Blankets. The global integration is achieved through a weighted voting mechanism that penalizes low-support edges via exponential decay, filters unreliable ones with an adaptive threshold, and ensures acyclicity using a Feedback Arc Set (FAS) algorithm. The framework is model-agnostic, imposing no assumptions on the inductive biases of base learners, is compatible with arbitrary data settings without requiring specific structural forms, and fully supports parallelization. We also theoretically establish finite-sample error bounds for VISTA, and prove its asymptotic consistency under mild conditions. Extensive experiments on both synthetic and real datasets consistently demonstrate the effectiveness of VISTA, yielding notable improvements in both accuracy and efficiency over a wide range of base learners.

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References · 33

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  2. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  3. 2013Causal Discovery with Continuous Additive Noise Modelscited by 252
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  8. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 159
  9. 2019Causal Discovery with Reinforcement Learningcited by 145
  10. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
  11. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 108
  12. 2020Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Datacited by 99
21 more
  1. 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 86
  2. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
  3. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  4. 2022Diffusion Models for Causal Discovery via Topological Orderingcited by 64
  5. 2015Learning Causal Graphs with Small Interventionscited by 63
  6. 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
  7. 2022Latent Hierarchical Causal Structure Discovery with Rank Constraintscited by 50
  8. 2013Learning Sparse Causal Models is not NP-hardcited by 48
  9. 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
  10. 2023Scalable Causal Discovery with Score Matchingcited by 42
  11. 2023Causal Discovery with Score Matching on Additive Models with Arbitrary Noisecited by 41
  12. 2020Active Structure Learning of Causal DAGs via Directed Clique Treecited by 38
  13. 2020On Low Rank Directed Acyclic Graphs and Causal Structure Learningcited by 23
  14. 2020A Recursive Markov Boundary-Based Approach to Causal Structure Learningcited by 21
  15. 2022Reliable Causal Discovery with Improved Exact Search and Weaker Assumptionscited by 19
  16. 2022Verification and search algorithms for causal DAGscited by 18
  17. 2019Learning big Gaussian Bayesian networks: partition, estimation, and fusioncited by 13
  18. 2017SADA: A General Framework to Support Robust Causation Discovery with Theoretical Guaranteecited by 8
  19. 2021Accelerating Recursive Partition-Based Causal Structure Learningcited by 4
  20. 2024Causal Discovery over High-Dimensional Structured Hypothesis Spaces with Causal Graph Partitioningcited by 4
  21. 2024Sample Efficient Bayesian Learning of Causal Graphs from Interventionscited by 4

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