Imagen de Portada

On Different Notions of Redundancy in Conditional-Independence-Based Discovery of Graphical Models

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Faller, Philipp M., Janzing, Dominik
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Cross-validating causal discovery via Leave-One-Variable-Out
por: Schkoda, Daniela, et al.
Publicado: (2024)

Self-Compatibility: Evaluating Causal Discovery without Ground Truth
por: Faller, Philipp M., et al.
Publicado: (2023)

From Guess2Graph: When and How Can Unreliable Experts Safely Boost Causal Discovery in Finite Samples?
por: Hiremath, Sujai, et al.
Publicado: (2025)

Quantifying intrinsic causal contributions via structure preserving interventions
por: Janzing, Dominik, et al.
Publicado: (2020)

Root Cause Analysis of Outliers in Unknown Cyclic Graphs
por: Schkoda, Daniela, et al.
Publicado: (2025)

Notes on Forré's Notion of Conditional Independence and Causal Calculus for Continuous Variables
por: Chen, Leihao
Publicado: (2026)

Toward Falsifying Causal Graphs Using a Permutation-Based Test
por: Eulig, Elias, et al.
Publicado: (2023)

Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks
por: Faller, Josua, et al.
Publicado: (2025)

Meaningful Causal Aggregation and Paradoxical Confounding
por: Zhu, Yuchen, et al.
Publicado: (2023)

Conditional Diffusion Models Based Conditional Independence Testing
por: Yang, Yanfeng, et al.
Publicado: (2024)

Graphical Modelling without Independence Assumptions for Uncentered Data
por: Andrew, Bailey, et al.
Publicado: (2024)

Causal Discovery with Fewer Conditional Independence Tests
por: Shiragur, Kirankumar, et al.
Publicado: (2024)

Toward Universal Laws of Outlier Propagation
por: Ebtekar, Aram, et al.
Publicado: (2025)

Causal Discovery via Conditional Independence Testing with Proxy Variables
por: Liu, Mingzhou, et al.
Publicado: (2023)

Fast Flow Matching based Conditional Independence Tests for Causal Discovery
por: Zhao, Shunyu, et al.
Publicado: (2026)

Causal vs. Anticausal merging of predictors
por: Mejia, Sergio Hernan Garrido, et al.
Publicado: (2025)

Efficient Ensemble Conditional Independence Test Framework for Causal Discovery
por: Guan, Zhengkang, et al.
Publicado: (2025)

Root Cause Analysis of Outliers with Missing Structural Knowledge
por: Orchard, William Roy, et al.
Publicado: (2024)

On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
por: Monés, Marc Franquesa, et al.
Publicado: (2026)

Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
por: Manten, Georg, et al.
Publicado: (2024)

DoWhy-GCM: An extension of DoWhy for causal inference in graphical causal models
por: Blöbaum, Patrick, et al.
Publicado: (2022)

Conditional Local Independence Testing for Itô processes with Applications to Dynamic Causal Discovery
por: Liu, Mingzhou, et al.
Publicado: (2025)

PAIR-CI: Calibrated Conditional Independence Testing for Causal Discovery with Incomplete Data
por: Robinson, Thomas S., et al.
Publicado: (2026)

An Asymmetric Independence Model for Causal Discovery on Path Spaces
por: Manten, Georg, et al.
Publicado: (2025)

False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening
por: Koka, Taulant, et al.
Publicado: (2024)

Interventional Causal Structure Discovery over Graphical Models with Convergence and Optimality Guarantees
por: Chengbo, Qiu, et al.
Publicado: (2024)

A Fast Kernel-based Conditional Independence test with Application to Causal Discovery
por: Schacht, Oliver, et al.
Publicado: (2025)

Automatic Parameter Selection for Non-Redundant Clustering
por: Leiber, Collin, et al.
Publicado: (2023)

Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model
por: Chen, Yujie, et al.
Publicado: (2025)

Score matching through the roof: linear, nonlinear, and latent variables causal discovery
por: Montagna, Francesco, et al.
Publicado: (2024)

Amortized Conditional Independence Testing
por: Duong, Bao, et al.
Publicado: (2025)

Multiply-Robust Causal Change Attribution
por: Quintas-Martinez, Victor, et al.
Publicado: (2024)

Conditional Independence Estimates for the Generalized Nonparanormal
por: Shah, Ujas, et al.
Publicado: (2025)

Practical Kernel Tests of Conditional Independence
por: Pogodin, Roman, et al.
Publicado: (2024)

Beyond Differences: Doubly Robust Meta-Learners for Ratio-Based Treatment Effects
por: Fuchs, Michael, et al.
Publicado: (2026)

Independent Component Discovery in Temporal Count Data
por: Chaussard, Alexandre, et al.
Publicado: (2026)

Assumption violations in causal discovery and the robustness of score matching
por: Montagna, Francesco, et al.
Publicado: (2023)

Score-based Generative Modeling for Conditional Independence Testing
por: Ren, Yixin, et al.
Publicado: (2025)

OMNI: Open-endedness via Models of human Notions of Interestingness
por: Zhang, Jenny, et al.
Publicado: (2023)

TorchSISSO: A PyTorch-Based Implementation of the Sure Independence Screening and Sparsifying Operator for Efficient and Interpretable Model Discovery
por: Muthyala, Madhav, et al.
Publicado: (2024)