Cover Image

Automating the Selection of Proxy Variables of Unmeasured Confounders

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Xie, Feng, Chen, Zhengming, Luo, Shanshan, Miao, Wang, Cai, Ruichu, Geng, Zhi
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Discovering Causal Relationships using Proxy Variables under Unmeasured Confounding
by: Wu, Yong, et al.
Published: (2025)

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

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

Differentiable Cyclic Causal Discovery Under Unmeasured Confounders
by: Sethuraman, Muralikrishnna G., et al.
Published: (2025)

Reinforcement Learning with Continuous Actions Under Unmeasured Confounding
by: Li, Yuhan, et al.
Published: (2025)

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

Simple yet Sharp Sensitivity Analysis for Any Contrast Under Unmeasured Confounding
by: Peña, Jose M.
Published: (2024)

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

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

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

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

Testability of Instrumental Variables in Additive Nonlinear, Non-Constant Effects Models
by: Guo, Xichen, et al.
Published: (2024)

Identification and Estimation of the Bi-Directional MR with Some Invalid Instruments
by: Xie, Feng, et al.
Published: (2024)

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

When Is Causal Inference Possible? A Statistical Test for Unmeasured Confounding
by: Liu, Muye, et al.
Published: (2025)

Partial Identification of Causal Effects Using Proxy Variables
by: Ghassami, AmirEmad, et al.
Published: (2023)

Robust Ultra-High-Dimensional Variable Selection With Correlated Structure Using Group Testing
by: Guo, Wanru, et al.
Published: (2026)

The Role of Measured Covariates in Assessing Sensitivity to Unmeasured Confounding
by: Dalal, Abhinandan, et al.
Published: (2026)

Penalized Generative Variable Selection
by: Wang, Tong, et al.
Published: (2024)

BVSIMC: Bayesian Variable Selection-Guided Inductive Matrix Completion for Improved and Interpretable Drug Discovery
by: Fan, Sijian, et al.
Published: (2026)

Identifying Causal Effects Using a Single Proxy Variable
by: Vollmer, Silvan, et al.
Published: (2026)

Towards Identifiability of Hierarchical Temporal Causal Representation Learning
by: Li, Zijian, et al.
Published: (2025)

SpaCE: The Spatial Confounding Environment
by: Tec, Mauricio, et al.
Published: (2023)

GST-UNet: A Neural Framework for Spatiotemporal Causal Inference with Time-Varying Confounding
by: Oprescu, Miruna, et al.
Published: (2025)

Diffusion-Driven High-Dimensional Variable Selection
by: Wang, Minjie, et al.
Published: (2025)

Robust Estimation and Model Selection for the Controlled Directed Effect with Unmeasured Mediator-Outcome Confounders
by: Orihara, Shunichiro, et al.
Published: (2024)

Nonparametric Identification and Inference for Counterfactual Distributions with Confounding
by: Sun, Jianle, et al.
Published: (2026)

Feature Attribution with Necessity and Sufficiency via Dual-stage Perturbation Test for Causal Explanation
by: Chen, Xuexin, et al.
Published: (2024)

Causal-learn: Causal Discovery in Python
by: Zheng, Yujia, et al.
Published: (2023)

Navigating Unmeasured Confounding in Quantitative Sociology: A Sensitivity Framework
by: Lin, Cheng, et al.
Published: (2023)

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

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

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

Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
by: Dharmakeerthi, Kulunu, et al.
Published: (2024)

Causal Inference with Categorical Unobserved Confounder via Mixture Learning
by: Saha, Aytijhya, et al.
Published: (2026)

Sensitivity Analysis to Unobserved Confounding with Copula-based Normalizing Flows
by: Balgi, Sourabh, et al.
Published: (2025)

Identifiable Deep Latent Variable Models for MNAR Data
by: Xie, Huiming, et al.
Published: (2026)

Linear Models, Variable Selection, Artificial Intelligence
by: Alrawkan, By Riyadh, et al.
Published: (2026)

Comparing Two Proxy Methods for Causal Identification
by: Guo, Helen, et al.
Published: (2025)

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