Imagen de Portada

Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Wu, Yufeng, Bhattacharya, Rohit
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Causal Effect Identification in LiNGAM Models with Latent Confounders
por: Tramontano, Daniele, et al.
Publicado: (2024)

Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks
por: Jin, Songyao, et al.
Publicado: (2025)

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

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

Causal Effect Estimation using identifiable Variational AutoEncoder with Latent Confounders and Post-Treatment Variables
por: Xie, Yang, et al.
Publicado: (2024)

Long-term Causal Inference via Modeling Sequential Latent Confounding
por: Chen, Weilin, et al.
Publicado: (2025)

On Theoretical Identifiability of Discrete Latent Causal Graphical Models
por: Lee, Seunghyun, et al.
Publicado: (2025)

Identification of Causal Direction under an Arbitrary Number of Latent Confounders
por: Chen, Wei, et al.
Publicado: (2025)

Causal Discovery with Mixed Latent Confounding via Precision Decomposition
por: Asiaee, Amir, et al.
Publicado: (2025)

Spectral Representation for Causal Estimation with Hidden Confounders
por: Sun, Haotian, et al.
Publicado: (2024)

Causal Fine-Tuning under Latent Confounded Shift
por: Yu, Jialin, et al.
Publicado: (2024)

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

Regression-Based Estimation of Causal Effects in the Presence of Selection Bias and Confounding
por: Hafer, Marlies, et al.
Publicado: (2025)

Latent Variable Modeling for Robust Causal Effect Estimation
por: Morimura, Tetsuro, et al.
Publicado: (2025)

Identifying and Estimating Causal Direct Effects Under Unmeasured Confounding
por: Boileau, Philippe, et al.
Publicado: (2026)

Identification of Average Causal Effects in Confounded Additive Noise Models
por: Elahi, Muhammad Qasim, et al.
Publicado: (2024)

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

Sequential Treatment Effect Estimation with Unmeasured Confounders
por: Wang, Yingrong, et al.
Publicado: (2025)

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

Causal and Counterfactual Views of Missing Data Models
por: Nabi, Razieh, et al.
Publicado: (2022)

Confounded Budgeted Causal Bandits
por: Jamshidi, Fateme, et al.
Publicado: (2024)

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

Response to Discussions of "Causal and Counterfactual Views of Missing Data Models"
por: Nabi, Razieh, et al.
Publicado: (2025)

RCT Rejection Sampling for Causal Estimation Evaluation
por: Keith, Katherine A., et al.
Publicado: (2023)

Gene Regulatory Network Inference in the Presence of Selection Bias and Latent Confounders
por: Luo, Gongxu, et al.
Publicado: (2025)

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confounding
por: Schkoda, Daniela, et al.
Publicado: (2024)

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

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

Characterization and Learning of Causal Graphs with Latent Confounders and Post-treatment Selection from Interventional Data
por: Luo, Gongxu, et al.
Publicado: (2025)

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning
por: Wu, Yuanchen, et al.
Publicado: (2025)

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

Estimating Dyadic Treatment Effects with Unknown Confounders
por: Hoshino, Tadao, et al.
Publicado: (2024)

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

Conditional Average Treatment Effect Estimation Under Hidden Confounders
por: Aloui, Ahmed, et al.
Publicado: (2025)

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

Proximal Causal Inference With Text Data
por: Chen, Jacob M., et al.
Publicado: (2024)

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

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

Bounds and Sensitivity Analysis of the Causal Effect Under Outcome-Independent MNAR Confounding
por: Peña, Jose M.
Publicado: (2024)

Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network
por: Weinstein, Bar, et al.
Publicado: (2025)