RKHS-based covariate balancing for survival causal effect estimation

Wu Xue (), Xiaoke Zhang (), Kwun Chuen Gary Chan () and Raymond K. W. Wong ()
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Wu Xue: Meta Platforms Inc.
Xiaoke Zhang: George Washington University
Kwun Chuen Gary Chan: University of Washington
Raymond K. W. Wong: Texas A &M University

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2024, vol. 30, issue 1, No 3, 34-58

Abstract: Abstract Survival causal effect estimation based on right-censored data is of key interest in both survival analysis and causal inference. Propensity score weighting is one of the most popular methods in the literature. However, since it involves the inverse of propensity score estimates, its practical performance may be very unstable, especially when the covariate overlap is limited between treatment and control groups. To address this problem, a covariate balancing method is developed in this paper to estimate the counterfactual survival function. The proposed method is nonparametric and balances covariates in a reproducing kernel Hilbert space (RKHS) via weights that are counterparts of inverse propensity scores. The uniform rate of convergence for the proposed estimator is shown to be the same as that for the classical Kaplan–Meier estimator. The appealing practical performance of the proposed method is demonstrated by a simulation study as well as two real data applications to study the causal effect of smoking on survival time of stroke patients and that of endotoxin on survival time for female patients with lung cancer respectively.

Keywords: Kaplan–Meier curve; Distributional treatment effects; Sobolev space; Non-convex optimization; 62D20; 62G05; 62N02; 62P10 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10985-023-09590-y

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