The e-BH procedure is an e-value-based multiple testing procedure that provably controls the false discovery rate (FDR) under any dependence structure between the e-values. Despite this appealing theoretical FDR control guarantee, the e-BH procedure often suffers from low power in practice. In this paper, we propose a general framework that boosts the power of e-BH without sacrificing its FDR control under arbitrary dependence. This is achieved by the technique of conditional calibration, where we take as input the e-values and calibrate them to be a set of "boosted e-values" that are guaranteed to be no less -- and are often more -- powerful than the original ones. Our general framework is explicitly instantiated in three classes of multiple testing problems: (1) testing under parametric models, (2) conditional independence testing under the model-X setting, and (3) model-free conformalized selection. Extensive numerical experiments show that our proposed method significantly improves the power of e-BH while continuing to control the FDR. We also demonstrate the effectiveness of our method through an application to an observational study dataset for identifying individuals whose counterfactuals satisfy certain properties.

翻译：e-BH程序是一种基于e值的多重检验方法，可在任意e值依赖结构下保证对错误发现率（FDR）的控制。尽管具有这一极具吸引力的理论FDR控制保证，但e-BH程序在实际应用中常因统计效力不足而受限。本文提出一个通用框架，能在不牺牲任意依赖结构下FDR控制的前提下提升e-BH的检验效力。该目标通过条件校准技术实现：该技术将原始e值作为输入，校准得到一组“增强型e值”，这些值不仅能保证不低于原始e值的统计效力，而且通常具有更强的检验能力。本通用框架在以下三类多重检验问题中得到具体实现：（1）参数模型下的假设检验，（2）模型-X设定下的条件独立性检验，（3）无模型构型选择。大量数值实验表明，所提方法在持续控制FDR的同时显著提升了e-BH的检验效力。通过将其应用于一项观察性研究数据集（旨在识别反事实满足特定属性的个体），我们进一步验证了该方法的有效性。