mlsanityData to 14 Oct 2026

Robust Causal Discovery under Imperfect Structural Constraints

arXiv 2511.06790 ↗First posted 10 Nov 2025cs.LG cs.AI stat.MLcited by 2

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Abstract (arXiv)

Robust causal discovery from observational data under imperfect prior knowledge remains a significant and largely unresolved challenge. Existing methods typically presuppose perfect priors or can only handle specific, pre-identified error types. And their performance degrades substantially when confronted with flawed constraints of unknown location and type. This decline arises because most of them rely on inflexible and biased thresholding strategies that may conflict with the data distribution. To overcome these limitations, we propose to harmonizes knowledge and data through prior alignment and conflict resolution. First, we assess the credibility of imperfect structural constraints through a surrogate model, which then guides a sparse penalization term measuring the loss between the learned and constrained adjacency matrices. We theoretically prove that, under ideal assumption, the knowledge-driven objective aligns with the data-driven objective. Furthermore, to resolve conflicts when this assumption is violated, we introduce a multi-task learning framework optimized via multi-gradient descent, jointly minimizing both objectives. Our proposed method is robust to both linear and nonlinear settings. Extensive experiments, conducted under diverse noise conditions and structural equation model types, demonstrate the effectiveness and efficiency of our method under imperfect structural constraints.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 38 of its 83 references that are in the map. First posted on arXiv 10 Nov 2025.

Builds on

38 of 83 references.

References · 38

  1. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  2. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  3. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  4. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
  5. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 331
  6. 2019Gradient-Based Neural DAG Learningcited by 227
  7. 2019Pareto Multi-Task Learningcited by 224
  8. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  9. 2021Towards Causal Representation Learningcited by 209
  10. 2019Learning Sparse Nonparametric DAGscited by 207
  11. 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 191
  12. 2013CAM: Causal additive models, high-dimensional order search and penalized regressioncited by 178
26 more
  1. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  2. 2021Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy To Gamecited by 161
  3. 2019Causal Discovery with Reinforcement Learningcited by 145
  4. 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 140
  5. 2020Learning the Pareto Front with Hypernetworkscited by 125
  6. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
  7. 2020DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networkscited by 66
  8. 2022Diffusion Models for Causal Discovery via Topological Orderingcited by 66
  9. 2021gCastle: A Python Toolbox for Causal Discoverycited by 61
  10. 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
  11. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  12. 2021DAGs with No Curl: An Efficient DAG Structure Learning Approachcited by 53
  13. 2022Large-Scale Differentiable Causal Discovery of Factor Graphscited by 47
  14. 2015A fast PC algorithm for high dimensional causal discovery with multi-core PCscited by 45
  15. 2023Structure Learning with Continuous Optimization: A Sober Look and Beyondcited by 36
  16. 2021The impact of prior knowledge on causal structure learningcited by 35
  17. 2021NTS-NOTEARS: Learning Nonparametric DBNs With Prior Knowledgecited by 33
  18. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Frameworkcited by 29
  19. 2023A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Modelscited by 28
  20. 2020On Low Rank Directed Acyclic Graphs and Causal Structure Learningcited by 23
  21. 2022Reliable Causal Discovery with Improved Exact Search and Weaker Assumptionscited by 19
  22. 2024LibMOON: A Gradient-based MultiObjective OptimizatioN Library in PyTorchcited by 10
  23. 2025When Selection Meets Intervention: Additional Complexities in Causal Discoverycited by 10
  24. 2021Effective and efficient structure learning with pruning and model averaging strategiescited by 7
  25. 2023Mitigating Prior Errors in Causal Structure Learning: A Resilient Approach via Bayesian Networkscited by 5
  26. 2024Weakly-supervised causal discovery based on fuzzy knowledge and complex data complementaritycited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Consistency evaluation of benchmarks used for causal discoverycited by 1
  2. 2026xWhyL: Causal Interactive Learningcited by 0