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arXiv Machine Learning
Oct 16

Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes

The paper introduces Proximal Survival Bridge learning (PSB), a flexible doubly robust framework for estimating causal effects on right‑censored time‑to‑event outcomes under unmeasured confounding. PSB builds on proximal causal inference to produce outcome‑bridge, treatment‑bridge, and doubly robust estimators, and offers two flexible bridge learners: PSB‑K, a kernel estimator with low‑rank time decomposition, and PSB‑N, a neural estimator trained with a projected maximum‑moment‑restriction loss. Experiments on a standard SEM benchmark, a proximal semi‑synthetic ACTG study, and the real‑world Framingham study show PSB‑K excels on SEM and ACTG data, while PSB‑N provides greater stability on Framingham, with additional analyses demonstrating robustness to proxy quality and censoring levels.

By Dhanajit Brahma, Ruolin Meng, Lawrence Carin, Ricardo Henao
benchmarks
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arXiv Machine Learning
Oct 12

Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring

The paper investigates efficient learning of survival analysis models when data are subject to outcome-dependent truncation and right-censoring. It demonstrates that Cox regression can be trained with first‑order optimization when the baseline hazard is known, but joint learning of the baseline hazard introduces strict suboptimal local minima. Experiments on four datasets show that accounting for truncation reduces prediction bias and yields orders‑of‑magnitude speedups over expectation maximization.

By Itai Zilberstein, Ioannis Anagnostides, Arman Kilic, Tuomas Sandholm
safety
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Hugging Face Trending Papers
Oct 8

Efficient Learning of Survival Analysis Models Under Outcome-Dependent Truncation and Censoring

The paper investigates efficient learning of survival analysis models when data are subject to outcome‑dependent truncation and right‑censoring. It shows that Cox regression can be trained with first‑order methods when the baseline hazard is known, but joint estimation of the baseline hazard introduces non‑convexity and local minima. Experiments on four datasets demonstrate that accounting for truncation reduces bias, works with neural networks, and yields orders‑of‑magnitude speedups over expectation‑maximization.

safety
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arXiv Machine Learning
Jun 4

SurvPFN: Towards Foundation Models for Survival Predictions

arXiv:2606. 04564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched.

By Samuel B\"ohm (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany), Lennart Purucker (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Frank Hutter (Department of Computer Science, University of Freiburg, Freiburg, Germany, PriorLabs, Freiburg, Germany), Pascal Schlosser (Institute of Epidemiology and Prevention, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, US, CIBSS - Centre for Integrative Biological Signalling Studies, University of Freiburg, Freiburg, Germany)
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