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:2606. 18281v1 Announce Type: cross Abstract: Conditional average treatment effects (CATEs) are central to treatment decision-making in personalized medicine.
arXiv:2606. 06393v1 Announce Type: new Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts.
arXiv:2609.22383v1 Announce Type: cross Abstract: Instrumental variable (IV) methods address treatment endogeneity, but with non-compliance and heterogeneous treatment effects a binary instrument gen...
arXiv:2606. 03689v1 Announce Type: cross Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs.
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.
The paper introduces new one‑step and targeted minimum loss‑based estimators for the average treatment effect and the average treatment effect on the treated under the front‑door criterion. These estimators use multiple parameterizations of the observed data distribution, can avoid modeling the mediator density, and are compatible with flexible machine‑learning nuisance estimation. The authors derive conditions for root‑n consistency, asymptotic linearity, and develop doubly robust tests for identification assumptions, demonstrating improved efficiency and favorable performance in simulations and real‑world applications in education and emergency medicine.
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.
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.
arXiv:2608. 06288v1 Announce Type: new Abstract: This work presents a novel attention-based framework for estimating the Individual Probability of Treatment Benefit (IPTB) in survival analysis contexts.
Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. However, in the presence of right censoring, the event time is only partially observed, rendering conventional scoring rules inapplicable in their standard form.
arXiv:2606. 08305v1 Announce Type: cross Abstract: Externally controlled survival trials are increasingly used when concurrent randomized controls are infeasible, particularly in oncology and rare-disease settings with time-to-event endpoints.