Data to 14 Oct 2026

Greedy Relaxations of the Sparsest Permutation Algorithm

arXiv 2206.05421 ↗First posted 11 Jun 2022cs.AIcited by 64

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Abstract (arXiv)

There has been an increasing interest in methods that exploit permutation reasoning to search for directed acyclic causal models, including the "Ordering Search" of Teyssier and Kohler and GSP of Solus, Wang and Uhler. We extend the methods of the latter by a permutation-based operation, tuck, and develop a class of algorithms, namely GRaSP, that are efficient and pointwise consistent under increasingly weaker assumptions than faithfulness. The most relaxed form of GRaSP outperforms many state-of-the-art causal search algorithms in simulation, allowing efficient and accurate search even for dense graphs and graphs with more than 100 variables.

Key facts. Cited by 64 papers in the map (data through 14 Oct 2026). Builds on 3 of its 43 references that are in the map. First posted on arXiv 11 Jun 2022.

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3 of 43 references.

References · 3

2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
2013Causal Networks: Semantics and Expressivenesscited by 67
2013A Transformational Characterization of Equivalent Bayesian Network Structurescited by 41

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 10; 2024: 18; 2025: 12; 2026: 21.

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2023Causal-learn: Causal Discovery in Pythoncited by 109
2022Diffusion Models for Causal Discovery via Topological Orderingcited by 66
2023Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Treescited by 38
2022Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescited by 30
2024Causal Concept Graph Models: Beyond Causal Opacity in Deep Learningcited by 29
2023Self-Compatibility: Evaluating Causal Discovery without Ground Truthcited by 22
2024Sample, estimate, aggregate: A recipe for causal discovery foundation modelscited by 21
2024Causal Discovery with Fewer Conditional Independence Testscited by 13
2024Recursive Causal Discoverycited by 12
2024Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insightcited by 12
2023Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Searchcited by 8
2025CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal modelscited by 8

38 more

2023CDANs: Temporal Causal Discovery from Autocorrelated and Non-Stationary Time Series Datacited by 7
2024Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscited by 6
2024Better Simulations for Validating Causal Discovery with the DAG-Adaptation of the Onion Methodcited by 6
2023Order-based Structure Learning with Normalizing Flowscited by 5
2024Hybrid Top-Down Global Causal Discovery with Local Search for Linear and Nonlinear Additive Noise Modelscited by 5
2024Are you doing better than random guessing? A call for using negative controls when evaluating causal discovery algorithmscited by 5
2026Causal Representation Learning from General Environments under Nonparametric Mixingcited by 5
2025Embracing Discrete Search: A Reasonable Approach to Causal Structure Learningcited by 4
2022Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutationcited by 3
2024Optimal estimation of Gaussian (poly)treescited by 3
2025On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Modelscited by 3
2025Causal Discovery and Counterfactual Reasoning to Optimize Persuasive Dialogue Policiescited by 3
2025Causal Structure and Representation Learning with Biomedical Applicationscited by 3
2026Exactly Computing do-Shapley Valuescited by 3
2026FoundCause: Causal Discovery with Latent Confounders from Observational Datacited by 3
2024Greedy equivalence search for nonparametric graphical modelscited by 2
2025Generative Framework for Personalized Persuasion: Inferring Causal, Counterfactual, and Latent Knowledgecited by 2
2025Efficient Latent Variable Causal Discovery: Combining Score Search and Targeted Testingcited by 2
2025Graph Learning Is Suboptimal in Causal Banditscited by 2
2025Causal Ordering for Structure Learning from Time Seriescited by 2
2026Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweightingcited by 2
2023Causal Razorscited by 1
2024Choosing DAG Models Using Markov and Minimal Edge Count in the Absence of Ground Truthcited by 1
2024LoSAM: Local Search in Additive Noise Models with Mixed Mechanisms and General Noise for Global Causal Discoverycited by 1
2024QWO: Speeding Up Permutation-Based Causal Discovery in LiGAMscited by 1
2025When Additive Noise Meets Unobserved Mediators: Bivariate Denoising Diffusion for Causal Discoverycited by 1
2025Scalable Causal Discovery from Recursive Nonlinear Data via Truncated Basis Function Scores and Testscited by 1
2026Learning Audio-Visual Embeddings with Inferred Latent Interaction Graphscited by 1
2026Effect-Level Validation for Causal Discoverycited by 1
2026Leveraging Large Language Models for Causal Discovery: a Constraint-based, Argumentation-driven Approachcited by 1
2026On the Number of Conditional Independence Tests in Constraint-based Causal Discoverycited by 1
2026CauTion: Knowing When to Trust LLMs for Ensemble Causal Discoverycited by 1
2026KG-SoftMAP: Soft Knowledge-Graph Priors for Bayesian Network Structure Learning from Sparse Discrete Datacited by 1
2023Causal Structure Learning by Using Intersection of Markov Blanketscited by 0
2023Distributionally Robust Skeleton Learning of Discrete Bayesian Networkscited by 0
2023Causal Discovery for fMRI data: Challenges, Solutions, and a Case Studycited by 0
2024Scalable Structure Learning for Sparse Context-Specific Systemscited by 0
2024Membership Testing in Markov Equivalence Classes via Independence Query Oraclescited by 0

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