Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustment

TL;DR

This paper introduces a novel kernel-based machine learning approach to estimate hazard ratios in observational studies, addressing interpretability issues of traditional Cox models by providing debiased estimators with proven convergence.

Contribution

It proposes a new method that abandons the baseline hazard in Cox models, using Neyman orthogonality and kernel methods for causal hazard ratio estimation in observational data.

Findings

The method accurately recovers true hazard ratios in simulations.

It minimizes bias compared to traditional approaches.

Provides a foundation for causal inference in observational epidemiology.

Abstract

Previous studies have shown that hazard ratios between treatment groups estimated with the Cox model are uninterpretable because the unspecified baseline hazard of the model fails to identify temporal change in the risk set composition due to treatment assignment and unobserved factors among multiple, contradictory scenarios. To alleviate this problem, especially in studies based on observational data with uncontrolled dynamic treatment and real-time measurement of many covariates, we propose abandoning the baseline hazard and using kernel-based machine learning to explicitly model the change in the risk set with or without latent variables. For this framework, we clarify the context in which hazard ratios can be causally interpreted, and then develop a method based on Neyman orthogonality to compute debiased maximum-likelihood estimators of hazard ratios, proving necessary convergence results. Numerical simulations confirm that the proposed method identifies the true hazard ratios with minimal bias. These results lay the foundation for developing a useful, alternative method for causal inference with uncontrolled, observational data in modern epidemiology.