mlsanityData to 14 Oct 2026

Causal Discovery with Latent Confounders Based on Higher-Order Cumulants

arXiv 2305.19582 ↗First posted 31 May 2023cs.LG cs.AI stat.MEcited by 16

Not on the map. Why?

Abstract (arXiv)

Causal discovery with latent confounders is an important but challenging task in many scientific areas. Despite the success of some overcomplete independent component analysis (OICA) based methods in certain domains, they are computationally expensive and can easily get stuck into local optima. We notice that interestingly, by making use of higher-order cumulants, there exists a closed-form solution to OICA in specific cases, e.g., when the mixing procedure follows the One-Latent-Component structure. In light of the power of the closed-form solution to OICA corresponding to the One-Latent-Component structure, we formulate a way to estimate the mixing matrix using the higher-order cumulants, and further propose the testable One-Latent-Component condition to identify the latent variables and determine causal orders. By iteratively removing the share identified latent components, we successfully extend the results on the One-Latent-Component structure to the Multi-Latent-Component structure and finally provide a practical and asymptotically correct algorithm to learn the causal structure with latent variables. Experimental results illustrate the asymptotic correctness and effectiveness of the proposed method.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 5 of its 44 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

5 of 44 references.

References · 5

  1. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  2. 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
  3. 2013ParceLiNGAM: A causal ordering method robust against latent confounderscited by 29
  4. 2020Causal Discovery with Unobserved Confounding and non-Gaussian Datacited by 25
  5. 2018High-Dimensional Causal Discovery Under non-Gaussianitycited by 24

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 7; 2026: 4.

16 papers.

Sort:

  1. 2024Local Causal Structure Learning in the Presence of Latent Variablescited by 11
  2. 2024Causal Effect Identification in LiNGAM Models with Latent Confounderscited by 7
  3. 2025Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounderscited by 7
  4. 2024Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confoundingcited by 6
  5. 2023Causal Discovery in Mixtures of Populationscited by 4
  6. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  7. 2026Latent Confounded Causal Discovery via Lie Bracket Geometrycited by 4
  8. 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulantscited by 3
  9. 2025Causal Discovery for Linear Non-Gaussian Models with Disjoint Cyclescited by 3
  10. 2024OCDB: Revisiting Causal Discovery with a Comprehensive Benchmark and Evaluation Frameworkcited by 2
  11. 2025Online Time Series Forecasting with Theoretical Guaranteescited by 2
  12. 2025Identification of Causal Direction under an Arbitrary Number of Latent Confounderscited by 1
4 more
  1. 2025KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamicscited by 0
  2. 2026From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variablescited by 0
  3. 2026Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Modelscited by 0
  4. 2026Linear Regression Beyond Covariance: Latent Slope Spectra from Higher-Order Cumulantscited by 0