arXiv:2609.14902v1 Announce Type: cross
Abstract: Shapley value (SV)-based methods are the prevailing framework for feature attribution in machine learning, yet existing population-level Shapley esti...
By Siqi Li, Wangxuan Fan, Yiming Li, Doudou Zhou, Molei Liu
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
By Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos
arXiv:2606. 00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models.
By Kara Liu, Maggie Wang, Russ B. Altman
The paper introduces Bayesian Propensity Score Predictive Inference (PSPI), an interaction‑based outcome modeling framework that uses Bayesian additive regression trees and natural cubic splines of the sampling propensity score to extend randomized trial results to broader target populations. PSPI includes variants that apply splines to the treatment effect component alone or to both prognostic and treatment effect components, enabling separate regularization of selection‑related variation and explicit extrapolation beyond boundary knots. Simulation studies demonstrate that spline‑based PSPI models outperform competing methods, achieving lower root mean squared errors and near‑nominal coverage, especially under substantial covariate shift, and in the CODIACS Vanguard trial application PSPI estimates larger intervention benefits than the unadjusted trial estimate.
By Jungang Zou, Joseph E. Schwartz, Roderick J. A. Little, Nathalie Moise, Qixuan Chen
arXiv:2606. 07677v1 Announce Type: cross Abstract: Electronic health records (EHR) pose large-scale multi-disease modeling problems in which many outcomes are rare and strongly influenced by shared risk factors.
By Shengxian Ding, Haonan Gao, Pangpang Liu, Xinyuan Tian, Yize Zhao
arXiv:2601. 20819v2 Announce Type: replace-cross Abstract: Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science.
By Yilin Song, Dan M. Kluger, Harsh Parikh, Tian Gu