# Doubly Robust Conformalized Survival Analysis with Right-Censored Data

arXiv: https://arxiv.org/abs/2412.09729 · HTML: https://mlsanity.com/paper/2412.09729/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2024.

## Built on by (most cited first, 6 of 6)

- [How Many Iterations to Jailbreak? Dynamic Budget Allocation for Multi-Turn LLM Evaluation](https://mlsanity.com/paper/2605.06605/) (2026)
- [Calibrated Predictive Lower Bounds on Time-to-Unsafe-Sampling in LLMs](https://mlsanity.com/paper/2506.13593/) (2025)
- [Conformal Bayes for Two-Sided Censored Gaussian Regression under Label Shift](https://mlsanity.com/paper/2607.02173/) (2026)
- [Elements of Conformal Prediction](https://mlsanity.com/paper/2603.23923/) (2026)
- [Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence](https://mlsanity.com/paper/2606.30500/) (2026)
- [A Multi-Cohort Validation of Censoring-Aware Conformal Lower Predictive Bounds for Pathology Survival Models](https://mlsanity.com/paper/2608.04025/) (2026)

## Abstract (arXiv)

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.