# auton-survival: an Open-Source Package for Regression, Counterfactual Estimation, Evaluation and Phenotyping with Censored Time-to-Event Data

arXiv: https://arxiv.org/abs/2204.07276 · HTML: https://mlsanity.com/paper/2204.07276/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Network](https://mlsanity.com/paper/1606.00931/) (2016)
- [Time-to-Event Prediction with Neural Networks and Cox Regression](https://mlsanity.com/paper/1907.00825/) (2019)
- [Deep Survival Machines: Fully Parametric Survival Regression and Representation Learning for Censored Data with Competing Risks](https://mlsanity.com/paper/2003.01176/) (2020)
- [Deep Survival Analysis](https://mlsanity.com/paper/1608.02158/) (2016)
- [Deep Cox Mixtures for Survival Regression](https://mlsanity.com/paper/2101.06536/) (2021)
- [Adversarial Time-to-Event Modeling](https://mlsanity.com/paper/1804.03184/) (2018)
- [Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect](https://mlsanity.com/paper/1710.05426/) (2017)
- [Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines](https://mlsanity.com/paper/1905.03297/) (2019)
- [Counterfactual Phenotyping with Censored Time-to-Events](https://mlsanity.com/paper/2202.11089/) (2022)
- [Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality](https://mlsanity.com/paper/2105.01009/) (2021)

## Built on by (most cited first, 17 of 17)

- [Deep Learning for Survival Analysis: A Review](https://mlsanity.com/paper/2305.14961/) (2023)
- [SurvSHAP(t): Time-dependent explanations of machine learning survival models](https://mlsanity.com/paper/2208.11080/) (2022)
- [Neural Fine-Gray: Monotonic neural networks for competing risks](https://mlsanity.com/paper/2305.06703/) (2023)
- [TorchSurv: A Lightweight Package for Deep Survival Analysis](https://mlsanity.com/paper/2404.10761/) (2024)
- [FPBoost: Fully Parametric Gradient Boosting for Survival Analysis](https://mlsanity.com/paper/2409.13363/) (2024)
- [TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction](https://mlsanity.com/paper/2508.00657/) (2025)
- [Using Geographic Location-based Public Health Features in Survival Analysis](https://mlsanity.com/paper/2304.07679/) (2023)
- [MENSA: A Multi-Event Network for Survival Analysis with Trajectory-based Likelihood Estimation](https://mlsanity.com/paper/2409.06525/) (2024)
- [Beyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis](https://mlsanity.com/paper/2504.17568/) (2025)
- [Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes](https://mlsanity.com/paper/2302.12504/) (2023)
- [Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling](https://mlsanity.com/paper/2311.01660/) (2023)
- [Censor Dependent Variational Inference](https://mlsanity.com/paper/2502.09591/) (2025)
- [Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation](https://mlsanity.com/paper/2211.11965/) (2022)
- [ICTSurF: Implicit Continuous-Time Survival Functions with Neural Networks](https://mlsanity.com/paper/2312.05818/) (2023)
- [Variational Deep Survival Machines: Survival Regression with Censored Outcomes](https://mlsanity.com/paper/2404.15595/) (2024)
- [Identifying treatment response subgroups in observational time-to-event data](https://mlsanity.com/paper/2408.03463/) (2024)
- [KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis](https://mlsanity.com/paper/2605.23082/) (2026)

## Abstract (arXiv)

Applications of machine learning in healthcare often require working with time-to-event prediction tasks including prognostication of an adverse event, re-hospitalization or death. Such outcomes are typically subject to censoring due to loss of follow up. Standard machine learning methods cannot be applied in a straightforward manner to datasets with censored outcomes. In this paper, we present auton-survival, an open-source repository of tools to streamline working with censored time-to-event or survival data. auton-survival includes tools for survival regression, adjustment in the presence of domain shift, counterfactual estimation, phenotyping for risk stratification, evaluation, as well as estimation of treatment effects. Through real world case studies employing a large subset of the SEER oncology incidence data, we demonstrate the ability of auton-survival to rapidly support data scientists in answering complex health and epidemiological questions.