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ParceLiNGAM: A causal ordering method robust against latent confounders

arXiv 1303.7410 ↗First posted 29 Mar 2013stat.MLcited by 29

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Abstract (arXiv)

We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually are violated. In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. The key idea is to detect latent confounders by testing independence between estimated external influences and find subsets (parcels) that include variables that are not affected by latent confounders. We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Mar 2013.

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Citing papers per year: 2021: 6; 2022: 1; 2023: 9; 2024: 6; 2025: 2; 2026: 5.

29 papers.

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  1. 2023Identifiability of latent-variable and structural-equation models: from linear to nonlinearcited by 58
  2. 2022Latent Hierarchical Causal Structure Discovery with Rank Constraintscited by 50
  3. 2023A Survey on Causal Reinforcement Learningcited by 43
  4. 2023A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variablescited by 33
  5. 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulantscited by 15
  6. 2023Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variablescited by 11
  7. 2023Identification of Causal Structure with Latent Variables Based on Higher Order Cumulantscited by 8
  8. 2021FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounderscited by 6
  9. 2024Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confoundingcited by 6
  10. 2021Learning linear non-Gaussian directed acyclic graph with diverging number of nodescited by 5
  11. 2025Causal Additive Models with Unobserved Causal Paths and Backdoor Pathscited by 5
  12. 2021Causal Discovery in Linear Structural Causal Models with Deterministic Relationscited by 4
17 more
  1. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  2. 2026Latent Confounded Causal Discovery via Lie Bracket Geometrycited by 4
  3. 2021Causal Order Identification to Address Confounding: Binary Variablescited by 2
  4. 2024Generalization of LiNGAM that allows confoundingcited by 2
  5. 2021Disentangling Observed Causal Effects from Latent Confounders using Method of Momentscited by 1
  6. 2021Application of quantum computing to a linear non-Gaussian acyclic model for novel medical knowledge discoverycited by 1
  7. 2023Recovering Linear Causal Models with Latent Variables via Cholesky Factorization of Covariance Matrixcited by 1
  8. 2023TSLiNGAM: DirectLiNGAM under heavy tailscited by 0
  9. 2023Recursive Counterfactual Deconfounding for Object Recognitioncited by 0
  10. 2024Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variablescited by 0
  11. 2024Redefining the Shortest Path Problem Formulation of the Linear Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and Path Enumerationcited by 0
  12. 2024Controlling for discrete unmeasured confounding in nonlinear causal modelscited by 0
  13. 2024Learning linear acyclic causal model including Gaussian noise using ancestral relationshipscited by 0
  14. 2025Addressing pitfalls in implicit unobserved confounding synthesis using explicit block hierarchical ancestral samplingcited by 0
  15. 2026MDL Meets Latent Confounders: LNML-based Causal Discoverycited by 0
  16. 2026Learning Sparsest Linear Causal DAGs with Latent Confounders via Higher-Order Cumulantscited by 0
  17. 2026MARCEDES: Score-based causal discovery under non-Gaussianity with continuous optimizationcited by 0