HSCI: Neyman-Orthogonal Causal Inference under High-Dimensional Proportional Hazards

2026-06-12

AI Summary

This study addresses the challenge of confounding bias in causal inference for survival analysis with high-dimensional covariates. The authors propose a High-dimensional Survival Causal Inference (HSCI) framework that integrates a sparse high-dimensional Cox proportional hazards model with a logistic propensity score model. By leveraging Neyman orthogonal score functions, cross-fitting, and regularized estimation, the method enables doubly robust inference for both treatment and covariate effects. It achieves √n-consistent asymptotic normality and consistent variance estimation while substantially reducing bias and maintaining stable confidence interval coverage. Extensive simulations and an empirical application to diffuse large B-cell lymphoma data demonstrate the method’s effectiveness and practical utility in high-dimensional biomedical survival analysis.

Abstract

Valid treatment effect inference in survival studies is fundamental yet challenging when the treatment assignments and outcomes are confounded by many baseline covariates. To this end, in this paper we propose a high-dimensional survival causal inference (HSCI) framework that delivers valid inference under a sparse high-dimensional Cox proportional hazards outcome model and a high-dimensional logistic propensity score working model. To mitigate the nuisance estimation bias, we develop a Neyman near-orthogonal score for the treatment effect and implement it with cross-fitting. Under doubly robust nuisance-rate conditions, we establish the root-n asymptotic normality and consistent variance estimation. We also extend the framework to inference on high-dimensional survival covariate effects. Simulation examples confirm that HSCI reduces sharply the bias relative to the regularized Cox estimators and maintains valid confidence interval coverage across different dimensionality, censoring, and misspecified propensity-model settings. An application to diffuse large-B-cell lymphoma data further showcases its value for high-dimensional biomedical survival studies.

Problem

• causal inference
• survival analysis
• high-dimensional data
• treatment effect
• confounding

Innovation

• Neyman-orthogonal score
• high-dimensional survival analysis
• causal inference
• cross-fitting
• doubly robust