The paper introduces a distributionally robust framework for survival analysis that simultaneously tackles latent subpopulation shift and outlier contamination. It employs an outer minimization to refine the nominal distribution by down-weighting contaminated samples and an inner maximization to target the most challenging subpopulation, directly handling non-decomposable survival losses such as the Cox partial log-likelihood. An alternating gradient-based algorithm, guided by KKT conditions, is developed, and experiments on simulated data and two benchmarks show improved worst-group performance and stable training under contamination.
By Seonghwi Kim, Sung Ho Jo, Minwoo Chae
The paper examines how fairness conclusions in ICU mortality prediction using MIMIC-IV depend on the choice of metrics and the granularity of demographic analysis. It compares predictive-utility and subgroup-error metrics across various fairness interventions and introduces a lightweight adaptation strategy that balances ethnicity, gender, and insurance representation without conditioning on mortality outcomes. The study finds that different interventions can be evaluated differently across accuracy, sensitivity, and false-positive rate, and that marginal demographic summaries may hide heterogeneous error patterns within intersectional subgroups.
By Abdullah Al Noman, Fahmid Al Rifat, Tahrima Hashem, Syed Muhammad Ibne Zulfiker, Rishov Paul, Tanzima HAshem
arXiv:2606. 01539v1 Announce Type: cross Abstract: Estimating the causal effect of time-varying treatments on survival outcomes in large observational studies is computationally demanding, particularly when outcomes are rare.
By Xiaohui Yin, Avijit Mitra, Ying Zhou, Kun Chen, Hong Yu
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 a method for selecting the best heterogeneous treatment effect (HTE) estimator from a set of candidates when the true treatment effect is unobserved. It frames estimator selection as a multiple testing problem and proposes a cross‑fitted, exponentially weighted test statistic that uses a two‑way sample splitting scheme to separate nuisance estimation from weight learning, ensuring stability for inference. The authors prove asymptotic familywise error rate control under mild conditions and demonstrate empirically that their procedure reduces false selections compared to common methods on ACIC 2016, IHDP, and Twins benchmarks.
By Jiayi Guo, Zijun Gao
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...
By Zixuan Yao, Guosheng Yin
The paper investigates demographic bias in large language model (LLM) predictions of ICU mortality and introduces Case-Based Prompting (CAP), a training‑free method that adds clinically similar historical cases as contextual evidence. CAP improves predictive performance on the MIMIC‑IV dataset, raising AUROC from 0.806 to 0.873 and AUPRC from 0.497 to 0.694, while reducing subgroup disparities for sex and White‑Black comparisons. The study also shows that simply removing demographic information does not uniformly enhance fairness or accuracy, and that feature‑dependence patterns remain consistent across groups.
By Gangxiong Zhang, Yongchao Long, Yuxi Zhou, Yong Zhang, Shenda Hong
arXiv:2605.10521v2 Announce Type: replace-cross
Abstract: As medical AI expands across diverse healthcare settings worldwide, equitable performance across patient populations is becoming essential to...
By Yiqi Tian, Jinwoong Park, Sangjoon Park, Bo Zeng, Pengfei Jin, Yujin Oh, Quanzheng Li
arXiv:2209. 01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed.
By Roshni Sahoo, Lihua Lei, Stefan Wager
arXiv:2608. 00701v1 Announce Type: cross Abstract: Reweighting source samples to match a target covariate distribution is a standard response to distribution shift when generalizing evidence from one population to another.
By Ying Jin, Ying Jin, Dominik Rothenh\"ausler
arXiv:2602.06924v3 Announce Type: replace
Abstract: Deep learning models trained to optimize average accuracy often exhibit systematic failures on particular subpopulations. In real-world settings li...
By Abinitha Gourabathina, Hyewon Jeong, Teya Bergamaschi, Marzyeh Ghassemi, Collin Stultz
arXiv:2605. 13430v3 Announce Type: replace-cross Abstract: Selection bias is pervasive in observational studies.
By Yiwen Qiu, Filip Kova\v{c}evi\'c, Shimeng Huang, Peter Spirtes, Francesco Locatello