mlsanityData to 14 Oct 2026

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models

arXiv 2210.11021 ↗First posted 20 Oct 2022cs.LG cs.AI stat.MLcited by 13

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Abstract (arXiv)

Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables are not the target variables of interest, but the imperfect measures of the target variables. Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error. We consider a specific formulation of the problem, where the unobserved target variables follow a linear non-Gaussian acyclic model, and the measurement process follows the random measurement error model. Existing methods on this formulation rely on non-scalable over-complete independent component analysis (OICA). In this work, we propose the Transformed Independent Noise (TIN) condition, which checks for independence between a specific linear transformation of some measured variables and certain other measured variables. By leveraging the non-Gaussianity and higher-order statistics of data, TIN is informative about the graph structure among the unobserved target variables. By utilizing TIN, the ordered group decomposition of the causal model is identifiable. In other words, we could achieve what once required OICA to achieve by only conducting independence tests. Experimental results on both synthetic and real-world data demonstrate the effectiveness and reliability of our method.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 8 of its 61 references that are in the map. First posted on arXiv 20 Oct 2022.

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8 of 61 references.

References · 8

  1. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  2. 2022Score matching enables causal discovery of nonlinear additive noise modelscited by 111
  3. 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
  4. 2013Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Modelscited by 12
  5. 2019Anchored Causal Inference in the Presence of Measurement Errorcited by 11
  6. 2015Anchored Discrete Factor Analysiscited by 9
  7. 2017Causal Discovery in the Presence of Measurement Error: Identifiability Conditionscited by 9
  8. 2018An Upper Bound for Random Measurement Error in Causal Discoverycited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 2; 2025: 4; 2026: 3.

13 papers.

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  1. 2023A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variablescited by 33
  2. 2023Unpaired Multi-Domain Causal Representation Learningcited by 32
  3. 2024Gene Regulatory Network Inference in the Presence of Dropouts: a Causal Viewcited by 11
  4. 2024Local Causal Discovery with Linear non-Gaussian Cyclic Modelscited by 7
  5. 2025Latent Variable Causal Discovery under Selection Biascited by 7
  6. 2025The Robustness of Differentiable Causal Discovery in Misspecified Scenarioscited by 4
  7. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  8. 2026Score-Based Causal Discovery of Latent Variable Causal Modelscited by 3
  9. 2025Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Modelscited by 2
  10. 2023On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factorscited by 1
  11. 2025Identification of Causal Direction under an Arbitrary Number of Latent Confounderscited by 1
  12. 2026Consistency evaluation of benchmarks used for causal discoverycited by 1
1 more
  1. 2023TSLiNGAM: DirectLiNGAM under heavy tailscited by 0