mlsanityData to 7 Oct 2026

Causal Effect Identification in LiNGAM Models with Latent Confounders

arXiv 2406.02049 ↗First posted 4 Jun 2024stat.ML cs.LG stat.MEcited by 7

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Abstract (arXiv)

We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 15 of its 51 references that are in the map. First posted on arXiv 4 Jun 2024.

Builds on

15 of 51 references.

References · 15

  1. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  2. 2020An Introduction to Proximal Causal Learningcited by 91
  3. 2022Causal Machine Learning for Healthcare and Precision Medicinecited by 68
  4. 2023Identifiability of latent-variable and structural-equation models: from linear to nonlinearcited by 58
  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 24
  7. 2023Causal Discovery with Latent Confounders Based on Higher-Order Cumulantscited by 15
  8. 2023A Cross-Moment Approach for Causal Effect Estimationcited by 9
  9. 2022Causal Discovery in Linear Latent Variable Models Subject to Measurement Errorcited by 7
  10. 2022Causal inference in drug discovery and developmentcited by 6
  11. 2023Confidence Sets for Causal Orderingscited by 6
  12. 2024Parameter identification in linear non-Gaussian causal models under general confoundingcited by 5
3 more
  1. 2023Sequential Underspecified Instrument Selection for Cause-Effect Estimationcited by 4
  2. 2023When does the ID algorithm fail?cited by 4
  3. 2023Proximal Causal Inference for Synthetic Control with Surrogatescited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 2.

7 papers.

  1. 2025Causal Additive Models with Unobserved Causal Paths and Backdoor Pathscited by 5
  2. 2025Causal Effect Identification in lvLiNGAM from Higher-Order Cumulantscited by 2
  3. 2024Causal Discovery in Linear Models with Unobserved Variables and Measurement Errorcited by 1
  4. 2025Estimating Treatment Effects with Independent Component Analysiscited by 0
  5. 2025Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variablescited by 0
  6. 2026Iterative Identification Closure: Amplifying Causal Identifiability in Linear SEMscited by 0
  7. 2026Function-Valued Causal Influence in Nonlinear Time Seriescited by 0