mlsanityData to 7 Oct 2026

Ordering-Based Causal Structure Learning in the Presence of Latent Variables

arXiv 1910.09014 ↗First posted 20 Oct 2019math.ST cs.LG stat.MLcited by 26

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Abstract (arXiv)

We consider the task of learning a causal graph in the presence of latent confounders given i.i.d.~samples from the model. While current algorithms for causal structure discovery in the presence of latent confounders are constraint-based, we here propose a score-based approach. We prove that under assumptions weaker than faithfulness, any sparsest independence map (IMAP) of the distribution belongs to the Markov equivalence class of the true model. This motivates the Sparsest Poset formulation - that posets can be mapped to minimal IMAPs of the true model such that the sparsest of these IMAPs is Markov equivalent to the true model. Motivated by this result, we propose a greedy algorithm over the space of posets for causal structure discovery in the presence of latent confounders and compare its performance to the current state-of-the-art algorithms FCI and FCI+ on synthetic data.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Oct 2019.

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Citing papers per year: 2021: 8; 2022: 4; 2023: 6; 2024: 4; 2025: 3; 2026: 1.

26 papers.

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  1. 2021A survey of Bayesian Network structure learningcited by 85
  2. 2021Ordering-Based Causal Discovery with Reinforcement Learningcited by 55
  3. 2022Causal Structure Learning: a Combinatorial Perspectivecited by 46
  4. 2021Integer Programming for Causal Structure Learning in the Presence of Latent Variablescited by 14
  5. 2023Computably Continuous Reinforcement-Learning Objectives are PAC-learnablecited by 13
  6. 2024Recursive Causal Discoverycited by 12
  7. 2022Novel Ordering-based Approaches for Causal Structure Learning in the Presence of Unobserved Variablescited by 10
  8. 2023Constraint-Free Structure Learning with Smooth Acyclic Orientationscited by 10
  9. 2021Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confoundingcited by 9
  10. 2023Expert-Aided Causal Discovery of Ancestral Graphscited by 8
  11. 2024Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)cited by 6
  12. 2022The Impact of Variable Ordering on Bayesian Network Structure Learningcited by 5
14 more
  1. 2021Causal Homotopycited by 4
  2. 2021Entropic Inequality Constraints from e-separation Relations in Directed Acyclic Graphs with Hidden Variablescited by 3
  3. 2023CMVAE: Causal Meta VAE for Unsupervised Meta-Learningcited by 3
  4. 2023s-ID: Causal Effect Identification in a Sub-Populationcited by 3
  5. 2025Score-informed Neural Operator for Enhancing Ordering-based Causal Discoverycited by 2
  6. 2025DAG DECORation: Continuous Optimization for Structure Learning under Hidden Confoundingcited by 2
  7. 2026Score-Based Causal Discovery of Latent Variable Causal Modelscited by 2
  8. 2021Improving Accuracy of Permutation DAG Search using Best Order Score Searchcited by 1
  9. 2023A Review and Roadmap of Deep Causal Model from Different Causal Structures and Representationscited by 1
  10. 2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMscited by 1
  11. 2025Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Modelscited by 1
  12. 2021Identification of Latent Variables From Graphical Model Residualscited by 0
  13. 2022Reinforcement Causal Structure Learning on Order Graphcited by 0
  14. 2024Machine Learning and Data Analysis Using Posets: A Surveycited by 0