Cover Image

A regression-based approach for bidirectional proximal causal inference in the presence of unmeasured confounding

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Min, Jiaqi, Zhang, Xueyue, Luo, Shanshan
Format:	Preprint
Published:	2025
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Bidirectional causal inference for binary outcomes in the presence of unmeasured confounding
by: Deng, Yafang, et al.
Published: (2026)

Spatial causal inference in the presence of unmeasured confounding and interference
by: Papadogeorgou, Georgia, et al.
Published: (2023)

Flexible sensitivity analysis for causal inference in observational studies subject to unmeasured confounding
by: Lu, Sizhu, et al.
Published: (2023)

Treatment effect extrapolation in the presence of unmeasured confounding
by: Riley, Stephanie, et al.
Published: (2025)

Exploiting independence constraints for efficient estimation of bounds on causal effects in the presence of unmeasured confounding
by: Chang, Ting-Hsuan, et al.
Published: (2026)

Controlling for discrete unmeasured confounding in nonlinear causal models
by: Burauel, Patrick, et al.
Published: (2024)

Interval identification of natural effects in the presence of outcome-related unmeasured confounding
by: Doretti, Marco, et al.
Published: (2024)

Estimating causal effects of continuous-time dynamic treatments with unmeasured confounders
by: Zhu, Haiyan, et al.
Published: (2026)

Simultaneous inference for generalized linear models with unmeasured confounders
by: Du, Jin-Hong, et al.
Published: (2023)

Interpretable sensitivity analysis for the Baron-Kenny approach to mediation with unmeasured confounding
by: Zhang, Mingrui, et al.
Published: (2022)

A joint modeling approach to treatment effects estimation with unmeasured confounders
by: Lee, Namhwa, et al.
Published: (2024)

An average-case sensitivity analysis for unmeasured confounding
by: Zhang, Yao, et al.
Published: (2022)

A proxy-based approach for unmeasured confounding in electronic health records research
by: Kottler, Haley Colgate, et al.
Published: (2025)

Semiparametric sensitivity analysis: unmeasured confounding in observational studies
by: Nabi, Razieh, et al.
Published: (2021)

A causal inference framework for spatial confounding
by: Gilbert, Brian, et al.
Published: (2021)

A confounding bridge approach for double negative control inference on causal effects
by: Miao, Wang, et al.
Published: (2018)

Partial identification and unmeasured confounding with multiple treatments and multiple outcomes
by: Kang, Suyeon, et al.
Published: (2023)

Mediation analysis with unmeasured confounding between parallel mediators and outcome
by: Shuai, Kang, et al.
Published: (2023)

Efficient estimation of average treatment effects with unmeasured confounding and proxies
by: Ai, Chunrong, et al.
Published: (2025)

Disentangling spatial interference and spatial confounding biases in causal inference
by: Ogunsola, Isqeel, et al.
Published: (2026)

Inverse regression for causal inference with multiple outcomes
by: Zhang, Wei, et al.
Published: (2025)

Regression-based proximal causal inference for right-censored time-to-event data
by: Li, Kendrick, et al.
Published: (2024)

Dynamical causality under invisible confounders
by: Yan, Jinling, et al.
Published: (2024)

Recovery and inference of causal effects with sequential adjustment for confounding and attrition
by: de Aguas, Johan, et al.
Published: (2024)

Valid causal inference with unobserved confounding in high-dimensional settings
by: Moosavi, Niloofar, et al.
Published: (2024)

Unifying regression-based and design-based causal inference in time-series experiments
by: Lin, Zhexiao, et al.
Published: (2025)

Efficiency-improved doubly robust estimation with non-confounding predictive covariates
by: Luo, Shanshan, et al.
Published: (2024)

Design-based theory for causal inference
by: Lu, Xin, et al.
Published: (2025)

Spatial causal inference in the presence of preferential sampling to study the impacts of marine protected areas
by: Son, Dongjae, et al.
Published: (2024)

crumble: A comprehensive framework for modern causal mediation analysis with intermediate confounding
by: Liu, Richard, et al.
Published: (2026)

An exhaustive selection of sufficient adjustment sets for causal inference
by: Luo, Wei, et al.
Published: (2024)

Generalizing and transporting causal inferences from randomized trials in the presence of trial engagement effects
by: Ung, Lawson, et al.
Published: (2024)

Penalized mixed models to adjust for batch effects and unobserved confounding in high dimensional regression
by: Lu, Yujing, et al.
Published: (2025)

On robust Bayesian causal inference
by: Alexopoulos, Angelos, et al.
Published: (2025)

Identification and estimation of causal peer effects using instrumental variables
by: Luo, Shanshan, et al.
Published: (2025)

Parameter identification in linear non-Gaussian causal models under general confounding
by: Tramontano, Daniele, et al.
Published: (2024)

Design-based theory for causal inference from adaptive experiments
by: Li, Xinran, et al.
Published: (2026)

Post-selection inference for causal effects after causal discovery
by: Chang, Ting-Hsuan, et al.
Published: (2024)

Identification and estimation of causal mechanisms in cluster-randomized trials with post-treatment confounding using Bayesian nonparametrics
by: Ohnishi, Yuki, et al.
Published: (2025)

Towards a unified approach to formal risk of bias assessments for causal and descriptive inference
by: Pescott, Oliver L., et al.
Published: (2023)