Copertina

Optimizing Graph Causal Classification Models: Estimating Causal Effects and Addressing Confounders

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Job, Simi, Tao, Xiaohui, Cai, Taotao, Xie, Haoran, Yong, Jianming, Wang, Xin
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Causal Neighbourhood Learning for Invariant Graph Representations
di: Job, Simi, et al.
Pubblicazione: (2026)

Towards Causal Classification: A Comprehensive Study on Graph Neural Networks
di: Job, Simi, et al.
Pubblicazione: (2024)

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

Exploring Causal Learning Through Graph Neural Networks: An In‐Depth Review
di: Simi Job, et al.
Pubblicazione: (2025)

Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence
di: Shaik, Thanveer, et al.
Pubblicazione: (2023)

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

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

Relational Causal Discovery with Latent Confounders
di: Negro, Matteo, et al.
Pubblicazione: (2025)

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

Estimating Causal Effects from Learned Causal Networks
di: Raichev, Anna, et al.
Pubblicazione: (2024)

Exploring Incremental Unlearning: Techniques, Challenges, and Future Directions
di: Qureshi, Sadia, et al.
Pubblicazione: (2025)

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

Compositional Models for Estimating Causal Effects
di: Pruthi, Purva, et al.
Pubblicazione: (2024)

Estimating Causal Effects in Partially Directed Parametric Causal Factor Graphs
di: Luttermann, Malte, et al.
Pubblicazione: (2024)

Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
di: Shaik, Thanveer, et al.
Pubblicazione: (2023)

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

Graph Neural Network Causal Explanation via Neural Causal Models
di: Behnam, Arman, et al.
Pubblicazione: (2024)

FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
di: Shaik, Thanveer, et al.
Pubblicazione: (2023)

Text Rationalization for Robust Causal Effect Estimation
di: Zhang, Lijinghua, et al.
Pubblicazione: (2025)

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

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

CausalPlayground: Addressing Data-Generation Requirements in Cutting-Edge Causality Research
di: Sauter, Andreas W M, et al.
Pubblicazione: (2024)

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

Estimating and Mitigating the Congestion Effect of Curbside Pick-ups and Drop-offs: A Causal Inference Approach
di: Liu, Xiaohui, et al.
Pubblicazione: (2022)

Causal Concept Graph Models: Beyond Causal Opacity in Deep Learning
di: Dominici, Gabriele, et al.
Pubblicazione: (2024)

SNAP: Sequential Non-Ancestor Pruning for Targeted Causal Effect Estimation With an Unknown Graph
di: Schubert, Mátyás, et al.
Pubblicazione: (2025)

Causally Fair Node Classification on Non-IID Graph Data
di: Dai, Yucong, et al.
Pubblicazione: (2025)

Heterophilic Graph Neural Networks Optimization with Causal Message-passing
di: Wang, Botao, et al.
Pubblicazione: (2024)

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

Disentangled Double Machine Learning for Accurate Causal Effect Estimation
di: Xiang, Guodu, et al.
Pubblicazione: (2026)

Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation
di: Mahajan, Divyat, et al.
Pubblicazione: (2022)

Causal Invariance and Counterfactual Learning Driven Cooperative Game for Multi-Label Classification
di: Fan, Yijia, et al.
Pubblicazione: (2025)

UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts
di: Wang, Tongze, et al.
Pubblicazione: (2026)

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

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

Causal Graph ODE: Continuous Treatment Effect Modeling in Multi-agent Dynamical Systems
di: Huang, Zijie, et al.
Pubblicazione: (2024)

Causality Enhanced Origin-Destination Flow Prediction in Data-Scarce Cities
di: Feng, Tao, et al.
Pubblicazione: (2025)

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

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

Generalized Independent Noise Condition for Estimating Causal Structure with Latent Variables
di: Xie, Feng, et al.
Pubblicazione: (2023)