Score-based Greedy Search for Structure Identification of Partially Observed Linear Causal Models
Not on the map. Why?
Abstract (arXiv)
Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solve this problem, and yet in practice these methods often face challenges related to multiple testing and error propagation. These issues could be mitigated by a score-based method and thus it has raised great attention whether there exists a score-based greedy search method that can handle the partially observed scenario. In this work, we propose the first score-based greedy search method for the identification of structure involving latent variables with identifiability guarantees. Specifically, we propose Generalized N Factor Model and establish the global consistency: the true structure including latent variables can be identified up to the Markov equivalence class by using score. We then design Latent variable Greedy Equivalence Search (LGES), a greedy search algorithm for this class of model with well-defined operators, which search very efficiently over the graph space to find the optimal structure. Our experiments on both synthetic and real-life data validate the effectiveness of our method (code will be publicly available).
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 42 of its 97 references that are in the map. First posted on arXiv 5 Oct 2025.
Builds on
42 of 97 references.
References · 42
- 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
- 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
- 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
- 2019Gradient-Based Neural DAG Learningcited by 227
- 2020Differentiable Causal Discovery from Interventional Datacited by 218
- 2019Learning Sparse Nonparametric DAGscited by 207
- 2020On the Role of Sparsity and DAG Constraints for Learning Linear DAGscited by 186
- 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
- 2022DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterizationcited by 135
- 2019Masked Gradient-Based Causal Structure Learningcited by 89
- 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
- 2020DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networkscited by 66
30 more
- 2020Differentiable Causal Discovery Under Unmeasured Confoundingcited by 56
- 2021Learning latent causal graphs via mixture oraclescited by 55
- 2022Latent Hierarchical Causal Structure Discovery with Rank Constraintscited by 50
- 2013Learning Sparse Causal Models is not NP-hardcited by 48
- 2015High-dimensional consistency in score-based and hybrid structure learningcited by 44
- 2019Learning Linear Non-Gaussian Causal Models in the Presence of Latent Variablescited by 39
- 2019Scaling structural learning with NO-BEARS to infer causal transcriptome networkscited by 39
- 2017Nested Markov Properties for Acyclic Directed Mixed Graphscited by 38
- 2020On the Convergence of Continuous Constrained Optimization for Structure Learningcited by 38
- 2023A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variablescited by 33
- 2023Unpaired Multi-Domain Causal Representation Learningcited by 32
- 2021Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Biascited by 29
- 2019Ordering-Based Causal Structure Learning in the Presence of Latent Variablescited by 26
- 2020Causal Discovery with Unobserved Confounding and non-Gaussian Datacited by 24
- 2022Truncated Matrix Power Iteration for Differentiable DAG Learningcited by 23
- 2021Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Biascited by 21
- 2022Differentiable Causal Discovery Under Latent Interventionscited by 21
- 2023NODAGS-Flow: Nonlinear Cyclic Causal Structure Learningcited by 20
- 2023Optimizing NOTEARS Objectives via Topological Swapscited by 17
- 2020Causal Discovery with Multi-Domain LiNGAM for Latent Factorscited by 13
- 2022Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Modelscited by 13
- 2021Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confoundingcited by 9
- 2015Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagramscited by 8
- 2024On the Parameter Identifiability of Partially Observed Linear Causal Modelscited by 7
- 2013Learning directed acyclic graphs based on sparsest permutationscited by 6
- 2025Analytic DAG Constraints for Differentiable DAG Learningcited by 5
- 2025Permutation-Based Rank Test in the Presence of Discretization and Application in Causal Discovery with Mixed Datacited by 4
- 2013Asymptotic Model Selection for Directed Networks with Hidden Variablescited by 2
- 2020Automatic and Efficient Variability-Aware Lifting of Functional Programscited by 2
- 2025A Sample Efficient Conditional Independence Test in the Presence of Discretizationcited by 1
Built on by
Citing papers per year: 2026: 1.
1 paper.