mlsanityData to 14 Oct 2026

A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables

arXiv 2312.11001 ↗First posted 18 Dec 2023cs.LG stat.MEcited by 33

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Abstract (arXiv)

Most existing causal discovery methods rely on the assumption of no latent confounders, limiting their applicability in solving real-life problems. In this paper, we introduce a novel, versatile framework for causal discovery that accommodates the presence of causally-related hidden variables almost everywhere in the causal network (for instance, they can be effects of observed variables), based on rank information of covariance matrix over observed variables. We start by investigating the efficacy of rank in comparison to conditional independence and, theoretically, establish necessary and sufficient conditions for the identifiability of certain latent structural patterns. Furthermore, we develop a Rank-based Latent Causal Discovery algorithm, RLCD, that can efficiently locate hidden variables, determine their cardinalities, and discover the entire causal structure over both measured and hidden ones. We also show that, under certain graphical conditions, RLCD correctly identifies the Markov Equivalence Class of the whole latent causal graph asymptotically. Experimental results on both synthetic and real-world personality data sets demonstrate the efficacy of the proposed approach in finite-sample cases.

Key facts. Cited by 33 papers in the map (data through 14 Oct 2026). Builds on 16 of its 53 references that are in the map. First posted on arXiv 18 Dec 2023.

Builds on

16 of 53 references.

References · 16

  1. 2013Causal Inference and Causal Explanation with Background Knowledgecited by 170
  2. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 166
  3. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  4. 2023Causal-learn: Causal Discovery in Pythoncited by 109
  5. 2013Learning Equivalence Classes of Bayesian Networks Structurescited by 77
  6. 2021Learning latent causal graphs via mixture oraclescited by 55
  7. 2022Latent Hierarchical Causal Structure Discovery with Rank Constraintscited by 51
  8. 2014Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Setscited by 48
  9. 2013A Transformational Characterization of Equivalent Bayesian Network Structurescited by 41
  10. 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
  11. 2013ParceLiNGAM: A causal ordering method robust against latent confounderscited by 29
  12. 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
4 more
  1. 2021The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variablescited by 15
  2. 2022Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Modelscited by 13
  3. 2013Calculation of Entailed Rank Constraints in Partially Non-Linear and Cyclic Modelscited by 12
  4. 2020Causal Clustering for 1-Factor Measurement Models on Data with Various Typescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 7; 2025: 13; 2026: 12.

33 papers.

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  1. 2024Causal Representation Learning from Multiple Distributions: A General Settingcited by 68
  2. 2024Learning Discrete Concepts in Latent Hierarchical Modelscited by 17
  3. 2024Discovering and Reasoning of Causality in the Hidden World with Large Language Modelscited by 10
  4. 2024On the Parameter Identifiability of Partially Observed Linear Causal Modelscited by 7
  5. 2024A Skewness-Based Criterion for Addressing Heteroscedastic Noise in Causal Discoverycited by 7
  6. 2025Learning General Causal Structures with Hidden Dynamic Process for Climate Analysiscited by 7
  7. 2025Latent Variable Causal Discovery under Selection Biascited by 7
  8. 2025Debiasing Reward Models by Representation Learning with Guaranteescited by 6
  9. 2025Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Datacited by 5
  10. 2026Causal Representation Learning from General Environments under Nonparametric Mixingcited by 5
  11. 2026From Reasoning Traces to Reusable Modules: Understanding Compositional Generalization in Language Model Reasoningcited by 5
  12. 2024Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challengescited by 4
21 more
  1. 2025The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representationscited by 4
  2. 2026Distributional Equivalence in Linear Non-Gaussian Latent-Variable Cyclic Causal Models: Characterization and Learningcited by 4
  3. 2024Differentiable Causal Discovery For Latent Hierarchical Causal Modelscited by 3
  4. 2026Score-Based Causal Discovery of Latent Variable Causal Modelscited by 3
  5. 2025Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Modelscited by 2
  6. 2026A General Representation-Based Approach to Multi-Source Domain Adaptationcited by 2
  7. 2023On the Three Demons in Causality in Finance: Time Resolution, Nonstationarity, and Latent Factorscited by 1
  8. 2025A Sample Efficient Conditional Independence Test in the Presence of Discretizationcited by 1
  9. 2025Learning by Analogy: A Causal Framework for Composition Generalizationcited by 1
  10. 2026Traceable Latent Variable Discovery Based on Multi-Agent Collaborationcited by 1
  11. 2026Consistency evaluation of benchmarks used for causal discoverycited by 1
  12. 2025On Theoretical Identifiability of Discrete Latent Causal Graphical Modelscited by 0
  13. 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networkscited by 0
  14. 2025Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglementcited by 0
  15. 2025KarmaTS: A Universal Simulation Platform for Multivariate Time Series with Functional Causal Dynamicscited by 0
  16. 2025Beyond the Black Box: Identifiable Interpretation and Control in Generative Models via Causal Minimalitycited by 0
  17. 2026A Recursive Decomposition Framework for Causal Structure Learning in the Presence of Latent Variablescited by 0
  18. 2026Causal Modeling of Selection in Evolutioncited by 0
  19. 2026Causal Discovery in the Era of Agentscited by 0
  20. 2026Hessian Rank Constraint for Learning Structure of Nonlinear Latent Variable Modelscited by 0
  21. 2026LUCID: Learning Under Confounding for Inference and Discovery in Time Seriescited by 0