Doubly Robust Conformalized Survival Analysis with Right-Censored Data

Matteo Sesia, Vladimir Svetnik
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:53989-54029, 2025.

Abstract

We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrictive type-I censoring scenarios. The proposed method imputes unobserved censoring times using a machine learning model, and then analyzes the imputed data using a survival model calibrated via weighted conformal inference. This approach is theoretically supported by an asymptotic double robustness property. Empirical studies on simulated and real data demonstrate that our method leads to relatively informative predictive inferences and is especially robust in challenging settings where the survival model may be inaccurate.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-sesia25a, title = {Doubly Robust Conformalized Survival Analysis with Right-Censored Data}, author = {Sesia, Matteo and Svetnik, Vladimir}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {53989--54029}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/sesia25a/sesia25a.pdf}, url = {https://proceedings.mlr.press/v267/sesia25a.html}, abstract = {We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrictive type-I censoring scenarios. The proposed method imputes unobserved censoring times using a machine learning model, and then analyzes the imputed data using a survival model calibrated via weighted conformal inference. This approach is theoretically supported by an asymptotic double robustness property. Empirical studies on simulated and real data demonstrate that our method leads to relatively informative predictive inferences and is especially robust in challenging settings where the survival model may be inaccurate.} }
Endnote
%0 Conference Paper %T Doubly Robust Conformalized Survival Analysis with Right-Censored Data %A Matteo Sesia %A Vladimir Svetnik %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-sesia25a %I PMLR %P 53989--54029 %U https://proceedings.mlr.press/v267/sesia25a.html %V 267 %X We present a conformal inference method for constructing lower prediction bounds for survival times from right-censored data, extending recent approaches designed for more restrictive type-I censoring scenarios. The proposed method imputes unobserved censoring times using a machine learning model, and then analyzes the imputed data using a survival model calibrated via weighted conformal inference. This approach is theoretically supported by an asymptotic double robustness property. Empirical studies on simulated and real data demonstrate that our method leads to relatively informative predictive inferences and is especially robust in challenging settings where the survival model may be inaccurate.
APA
Sesia, M. & Svetnik, V.. (2025). Doubly Robust Conformalized Survival Analysis with Right-Censored Data. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:53989-54029 Available from https://proceedings.mlr.press/v267/sesia25a.html.

Related Material