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Decoupled Causal Discovery

arXiv 2609.23535 ↗First posted 20 Sep 2026cs.LG stat.MLcited by 0

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Abstract (arXiv)

Causal discovery from observational data is a fundamental yet challenging task in scientific research. While existing approaches are primarily based on conditional independence tests, structure scores, or restrictive functional assumptions, we propose Decoupled Causal Discovery (DCD), a novel decoupling-based perspective that does not rely on these methodologies. DCD directly identifies the Markov boundary (MB) by decoupling non-target variables via weighting functions, such that only variables within the MB preserve dependence with the target under the decoupled distribution. Building on this, DCD iteratively constructs the Completed Partially Directed Acyclic Graph (CPDAG) by exploiting structural asymmetries within the MBs. We establish the theoretical identifiability, soundness, and completeness of DCD. Empirical evaluations demonstrate that DCD achieves strong performance, particularly excelling in challenging noise regimes.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 57 references that are in the map. First posted on arXiv 20 Sep 2026.

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References · 23

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2019Gradient-Based Neural DAG Learningcited by 227
  3. 2021D'ya like DAGs? A Survey on Structure Learning and Causal Discoverycited by 211
  4. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  5. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 187
  6. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
  7. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
  8. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  9. 2017Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discoverycited by 76
  10. 2019Stable Learning via Sample Reweightingcited by 70
  11. 2018Stable Prediction across Unknown Environmentscited by 55
  12. 2017Causally Regularized Learning with Agnostic Data Selection Biascited by 29
11 more
  1. 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
  2. 2021Local permutation tests for conditional independencecited by 23
  3. 2020A Recursive Markov Boundary-Based Approach to Causal Structure Learningcited by 21
  4. 2021A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalizationcited by 19
  5. 2021Learning Bayesian Networks in the Presence of Structural Side Informationcited by 12
  6. 2024Recursive Causal Discoverycited by 12
  7. 2022Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variablescited by 10
  8. 2025Distinguishing Cause from Effect with Causal Velocity Modelscited by 6
  9. 2025Efficient Ensemble Conditional Independence Test Framework for Causal Discoverycited by 5
  10. 2025Causal Discovery via Quantile Partial Effectcited by 4
  11. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4

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