arXiv Machine Learning
Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
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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.
arXiv Machine Learning
Jun 18
A Guide to Estimating Conditional Average Treatment Effects in Competing Risks Settings
arXiv:2606. 18281v1 Announce Type: cross Abstract: Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine.
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Proper Scoring Rules for Right-Censored Survival Data
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.
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Bridging Local and Population Causal Effects: A Proximal Instrumental Variable Approach
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arXiv AI
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Staying Alive: Uncensored Survival Analysis with Tabular Foundation Models
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arXiv Machine Learning
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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.