Multiply-robust Estimator of Cumulative Incidence Function Difference for Right-Censored Competing Risks Data
右删失竞争风险数据中累积发生函数差的多重稳健估计
In causal inference, estimating the average treatment effect is a central objective, and in the context of competing risks data, this effect can be quantified by the cause-specific cumulative incidence function (CIF) difference. While doubly robust estimators give a more robust way to estimate the causal effect from the observational study, they remain inconsistent if both models are misspecified. To improve the robustness, we develop a multiply robust estimator for the…