← Back to all news
arXiv Statistics ML By Sunny Yang, Weiyan Zhao

When does conformal calibration need censoring weights? Cause-of-failure prediction sets under competing risks

Read the original on arXiv Statistics ML →

The paper investigates when conformal calibration for cause‑of‑failure prediction sets under competing risks requires censoring weights. It shows that using only complete‑case calibration can lead to coverage that is either above or below the nominal level, especially when a substantial proportion of subjects are event‑free at the prediction horizon. The authors demonstrate through simulations that incorporating weights from a correctly specified censoring model restores nominal coverage, and they provide a finite‑sample lower bound that penalises censoring‑model error, highlighting the risk of under‑coverage when the model is misspecified.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Statistics ML.

Related stories

arXiv Machine Learning
Jul 22

When Are Scoring Rules Proper? Bridging Theory and Practice in Survival Model Evaluation

arXiv:2212. 05260v4 Announce Type: replace-cross Abstract: Proper scoring rules encourage probabilistic predictions that match the true underlying distribution and are central to model evaluation, with increasing relevance in automated workflows such as AutoML.

By John Zobolas, Raphael Sonabend, Riccardo De Bin, Johannes Piller, Philipp Kopper, Lukas Burk, Andreas Bender
More like this →
arXiv Machine Learning
Sep 29

Missingness-Aware Conformal Prediction Under Cross-Hospital Distribution Shift

The paper introduces a missingness‑aware conformal calibration method for mortality prediction that accounts for cross‑hospital distribution shifts. By selecting a measurement on an independent sample, grouping patients by whether that measurement is recorded, and applying Mondrian calibration within each group, the method avoids reusing calibration outcomes. Experiments on eICU and MIMIC‑IV data show that, compared to pooled calibration, it reduces the worst‑group coverage gap by a median of 1.9 percentage points across six settings, though the benefit varies with predictor and hospital.

By Liang You, Dongwen Ou, Hengyu Shi, Siyuan Dai
More like this →