Post-Hoc Self-Correction for Conditional Calibration of Survival Models

We propose a post-hoc $self-correction$ procedure that moves a fitted survival model toward conditional calibration by transforming its estimated conditional cumulative distribution function (CDF) separately for each covariate value. The transformation is a nonparametric calibration map built from the conditional D-calibration criterion in survival analysis. At the population level, a single application recovers the true conditional CDF when there is no censoring. Under right censoring, each application reduces the discrepancy from the true conditional CDF by a factor determined by the censoring mechanism, and repeated applications converge to the true conditional CDF under conditions on the time horizon. We estimate the map using kernel weighting in a reduced covariate space. Experiments on ten datasets and six survival models show that a single application of the estimated map improves conditional calibration and the integrated Brier score in most pairwise comparisons with existing post-hoc methods, while maintaining comparable discrimination.