Proximal Causal Inference for Censored Data with an Application to Right Heart Catheterization Data

Yue Hu, Yuanshan Gao and Minhao Qi ()
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Yue Hu: School of Management, Zhejiang University, Hangzhou 310058, China
Yuanshan Gao: Center for Data Science, Zhejiang University, Hangzhou 310058, China
Minhao Qi: School of Management, Zhejiang University, Hangzhou 310058, China

Stats, 2025, vol. 8, issue 3, 1-22

Abstract: In observational causal inference studies, unmeasured confounding remains a critical threat to the validity of effect estimates. While proximal causal inference (PCI) has emerged as a powerful framework for mitigating such bias through proxy variables, existing PCI methods cannot directly handle censored data. This article develops a unified proximal causal inference framework that simultaneously addresses unmeasured confounding and right-censoring challenges, extending the proximal causal inference literature. Our key contributions are twofold: (i) We propose novel identification strategies and develop two distinct estimators for the censored-outcome bridge function and treatment confounding bridge function, resolving the fundamental challenge of unobserved outcomes; (ii) To improve robustness against model misspecification, we construct a robust proximal estimator and establish uniform consistency for all proposed estimators under mild regularity conditions. Through comprehensive simulations, we demonstrate the finite-sample performance of our methods, followed by an empirical application evaluating right heart catheterization effectiveness in critically ill ICU patients.

Keywords: proximal causal inference; censored data; identification; unmeasured confounding (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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