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.
By Lev V. Utkin, Stanislav K. Kogan, Andrei V. Konstantinov
The paper introduces a new framework for identifying average dose-response functions in the presence of unmeasured confounding by using instrumental variables. It defines a uniform regular weighting function and partitions the treatment space into open sets where local identification is possible. For estimation, the authors propose an augmented inverse probability weighted score within a debiased machine learning setting, along with practical guidance for constructing weighting functions, falsification tests for the additive IV condition, and asymptotic theory for kernel regression or empirical risk minimization estimators.
By Shuyuan Chen, Peng Zhang, Yifan Cui
arXiv:2608. 19383v1 Announce Type: cross Abstract: Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population.
By Jay Jojo Cheng, Guanhua Chen
arXiv:2606. 03332v1 Announce Type: new Abstract: Probabilistic models are typically trained using task-agnostic objectives like log-loss, which can lead to significant errors in downstream estimation.
By Roman Plaud, Alexandre Perez-Lebel, Antoine Saillenfest, Thomas Bonald, Marine Le Morvan, Ga\"el Varoquaux, Matthieu Labeau
Generative modeling has shown increasing promise for predicting cellular perturbation effects under chemical compound treatments. Existing approaches either model perturbation as a distribution-to-dis...
arXiv:2608. 19501v1 Announce Type: cross Abstract: Diagnostic medical tests and devices provide useful information for evaluating the potential benefits and risks of therapeutic treatments.
By Wenxin Zhang, Rachael Phillips, Mark van der Laan