CoxAIPW - Doubly Robust Inference for Cox Marginal Structural Model with
Informative Censoring
Doubly robust estimation and inference of log hazard ratio
under the Cox marginal structural model with informative
censoring. An augmented inverse probability weighted estimator
that involves 3 working models, one for conditional failure
time T, one for conditional censoring time C and one for
propensity score. Both models for T and C can depend on both a
binary treatment A and additional baseline covariates Z, while
the propensity score model only depends on Z. With the help of
cross-fitting techniques, achieves the rate-doubly robust
property that allows the use of most machine learning or
non-parametric methods for all 3 working models, which are not
permitted in classic inverse probability weighting or doubly
robust estimators. When the proportional hazard assumption is
violated, CoxAIPW estimates a causal estimated that is a
weighted average of the time-varying log hazard ratio.
Reference: Luo, J. (2023). Statistical Robustness - Distributed
Linear Regression, Informative Censoring, Causal Inference, and
Non-Proportional Hazards [Unpublished doctoral dissertation].
University of California San Diego.; Luo & Xu (2022)
<doi:10.48550/arXiv.2206.02296>; Rava (2021)
<https://escholarship.org/uc/item/8h1846gs>.