RKHS-based covariate balancing for survival causal effect estimation

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.

Automated summary

This paper proposes a nonparametric covariate balancing method to estimate the counterfactual survival function by balancing covariates in a reproducing kernel Hilbert space (RKHS) using weights that are counterparts of inverse propensity scores. The method addresses the instability issue of the propensity score weighting method when there is limited covariate overlap between treatment and control groups. Simulation study and real data applications on smoking's causal effect on stroke patients' survival time and endotoxin's causal effect on female lung cancer patients' survival time demonstrate the appealing practical performance of the proposed method.