mlsanityData to 14 Oct 2026

Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants

arXiv 2506.05202 ↗First posted 5 Jun 2025stat.ML cs.LG stat.MEcited by 3

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Abstract (arXiv)

This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups that are challenging in the presence of latent confounding: (1) a single proxy variable that may causally influence the treatment and (2) underspecified instrumental variable cases where fewer instruments exist than treatments. We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods. Experimental results demonstrate the accuracy and robustness of our approaches compared to existing methods, advancing the theoretical and practical understanding of causal inference in linear systems with latent confounders.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 57 references that are in the map. First posted on arXiv 5 Jun 2025.

Builds on

18 of 57 references.

References · 18

  1. 2017Avoiding Discrimination through Causal Reasoningcited by 194
  2. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 139
  3. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  4. 2020Semiparametric proximal causal inferencecited by 59
  5. 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
  6. 2020Causal Discovery with Unobserved Confounding and non-Gaussian Datacited by 25
  7. 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulantscited by 16
  8. 2022Identifiability of Sparse Causal Effects using Instrumental Variablescited by 9
  9. 2023A Cross-Moment Approach for Causal Effect Estimationcited by 9
  10. 2023Identification of Causal Structure with Latent Variables Based on Higher Order Cumulantscited by 8
  11. 2022Causal Discovery in Linear Latent Variable Models Subject to Measurement Errorcited by 7
  12. 2024Causal Effect Identification in LiNGAM Models with Latent Confounderscited by 7
6 more
  1. 2022Causal inference in drug discovery and developmentcited by 6
  2. 2023Confidence Sets for Causal Orderingscited by 6
  3. 2024Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confoundingcited by 6
  4. 2024Parameter identification in linear non-Gaussian causal models under general confoundingcited by 5
  5. 2023Sequential Underspecified Instrument Selection for Cause-Effect Estimationcited by 4
  6. 2024Targeted Sequential Indirect Experiment Designcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  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. 2026Linear Regression Beyond Covariance: Latent Slope Spectra from Higher-Order Cumulantscited by 0