Cover Image

Relational Causal Discovery with Latent Confounders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Negro, Matteo, Piras, Andrea, Ahsan, Ragib, Arbour, David, Zheleva, Elena
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Federated Causal Discovery Across Heterogeneous Datasets under Latent Confounding
by: Hahn, Maximilian, et al.
Published: (2026)

dcFCI: Robust Causal Discovery Under Latent Confounding, Unfaithfulness, and Mixed Data
by: Ribeiro, Adèle H., et al.
Published: (2025)

Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version)
by: Ma, Pingchuan, et al.
Published: (2024)

See Me and Believe Me: Causality and Intersectionality in Testimonial Injustice in Healthcare
by: Andrews, Kenya S., et al.
Published: (2024)

Physical Simulators as Do-Operators: Causal Discovery under Latent Confounders for AI-for-Science
by: Okita, Tsuyoshi
Published: (2026)

InvarGC: Invariant Granger Causality for Heterogeneous Interventional Time Series under Latent Confounding
by: Zhang, Ziyi, et al.
Published: (2025)

A Full DAG Score-Based Algorithm for Learning Causal Bayesian Networks with Latent Confounders
by: Gonzales, Christophe, et al.
Published: (2024)

Causal Structure Representation Learning of Confounders in Latent Space for Recommendation
by: Xu, Hangtong, et al.
Published: (2023)

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

Confounded Causal Imitation Learning with Instrumental Variables
by: Zeng, Yan, et al.
Published: (2025)

Temporal Latent Variable Structural Causal Model for Causal Discovery under External Interferences
by: Cai, Ruichu, et al.
Published: (2025)

Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders
by: Job, Simi, et al.
Published: (2026)

TriOpt: A Scalable Algorithm for Linear Causal Discovery
by: Joy, Rafat Ashraf, et al.
Published: (2026)

Learning Exposure Mapping Functions for Inferring Heterogeneous Peer Effects
by: Adhikari, Shishir, et al.
Published: (2025)

Data-Driven Estimation of Heterogeneous Treatment Effects
by: Tran, Christopher, et al.
Published: (2023)

Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data
by: Wang, Danyang, et al.
Published: (2024)

LLM-based Agents for Automated Confounder Discovery and Subgroup Analysis in Causal Inference
by: Lee, Po-Han, et al.
Published: (2025)

Efficient Latent Variable Causal Discovery: Combining Score Search and Targeted Testing
by: Ramsey, Joseph, et al.
Published: (2025)

From Local to Cluster: A Unified Framework for Causal Discovery with Latent Variables
by: Li, Zongyu
Published: (2026)

Information-Theoretic Causal Bounds under Unmeasured Confounding
by: Jung, Yonghan, et al.
Published: (2026)

Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
by: Shao, Daqian, et al.
Published: (2025)

Towards Robust Trajectory Representations: Isolating Environmental Confounders with Causal Learning
by: Luo, Kang, et al.
Published: (2024)

A Recipe for Causal Graph Regression: Confounding Effects Revisited
by: Yin, Yujia, et al.
Published: (2025)

LatentBreak: Jailbreaking Large Language Models through Latent Space Feedback
by: Mura, Raffaele, et al.
Published: (2025)

Local Causal Discovery for Statistically Efficient Causal Inference
by: Schubert, Mátyás, et al.
Published: (2025)

Heterogeneous Causal Discovery of Repeated Undesirable Health Outcomes
by: Adhikari, Shishir, et al.
Published: (2025)

Separating and Learning Latent Confounders to Enhancing User Preferences Modeling
by: Xu, Hangtong, et al.
Published: (2023)

Differentiable Constraint-Based Causal Discovery
by: Zhou, Jincheng, et al.
Published: (2025)

Linear Causal Discovery with Interventional Constraints
by: Guo, Zhigao, et al.
Published: (2025)

Multi-Domain Causal Discovery in Bijective Causal Models
by: Jalaldoust, Kasra, et al.
Published: (2025)

Learning to Defer for Causal Discovery with Imperfect Experts
by: Clivio, Oscar, et al.
Published: (2025)

Federated Causal Discovery from Heterogeneous Data
by: Li, Loka, et al.
Published: (2024)

CauScale: Neural Causal Discovery at Scale
by: Peng, Bo, et al.
Published: (2026)

Challenges and Considerations in the Evaluation of Bayesian Causal Discovery
by: Mamaghan, Amir Mohammad Karimi, et al.
Published: (2024)

Deep Causal Learning: Representation, Discovery and Inference
by: Deng, Zizhen, et al.
Published: (2022)

Causal Discovery by Interventions via Integer Programming
by: Elrefaey, Abdelmonem, et al.
Published: (2024)

Mask2Cause: Causal Discovery via Adjacency Constrained Causal Attention
by: Muhammad, Omar, et al.
Published: (2026)

Causal Fairness under Unobserved Confounding: A Neural Sensitivity Framework
by: Schröder, Maresa, et al.
Published: (2023)

Causal Discovery under Latent Class Confounding
by: Mazaheri, Bijan, et al.
Published: (2023)

Local Causal Structure Learning in the Presence of Latent Variables
by: Xie, Feng, et al.
Published: (2024)