Identification and Inference for Structural Accelerated Failure Time Models via Instrument Interactions

2026-05-27

AI Summary

This study addresses causal inference for time-to-event outcomes in the presence of unmeasured confounding and right censoring. The authors propose a novel identification strategy based on instrumental variable interactions, tailored to structural accelerated failure time models, which circumvents conventional instrumental variable validity assumptions. By integrating augmented inverse probability of censoring weighting, generalized empirical likelihood estimation, and Neyman-orthogonal moment conditions, the method achieves double robustness and orthogonality under multiple weak moment conditions. Theoretical analysis establishes consistency and asymptotic normality of the resulting estimator. Extensive simulations demonstrate superior performance across varying censoring rates and instrumental variable configurations, and the approach is successfully applied to real-world data from the UK Biobank.

Abstract

We study causal inference for time-to-event outcomes under right censoring in the presence of unmeasured confounding. Focusing on structural accelerated failure time models, we develop an identification and inference framework that exploits interactions among instrumental variables. The proposed approach does not rely on classical instrumental variable validity and yields valid causal inference under both valid and invalid instruments, provided that the interaction-based identification condition holds. To accommodate right censoring, we construct a censoring-adjusted observed data moment function using an augmented inverse probability censoring weighting approach. The resulting moment function is Neyman orthogonal with respect to nuisance functions and enjoys a double robustness property, enabling valid inference under flexible nuisance estimation. Estimation and inference are conducted using generalized empirical likelihood, which is well suited to settings with many potentially weak interaction-based moment conditions. We establish consistency, and asymptotic normality under many weak moment asymptotics, and develop diagnostic tools to assess interaction-based identification strength and overidentifying restrictions. Simulation studies demonstrate favorable finite sample performance across a range of censoring rates and instrument configurations. An application to UK Biobank data illustrates the practical relevance of the proposed method for causal survival analysis in large-scale observational studies.

Problem

• causal inference
• time-to-event outcomes
• unmeasured confounding
• right censoring
• instrumental variables

Innovation

• instrument interactions
• accelerated failure time model
• double robustness
• Neyman orthogonality
• generalized empirical likelihood