Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing
双重变量重要性匹配:区分事件概率与发生时间的因果效应
In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a cured subpopulation in which the event of interest never occurs. In such settings, treatment may have distinct effects on (1) the probability of being cured and (2) the event timing among non-cured individuals. Standard survival analysis and causal inference methods typically do not separate cured…