# Covariate powered cross-weighted multiple testing

arXiv: https://arxiv.org/abs/1701.05179 · HTML: https://mlsanity.com/paper/1701.05179/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2017.

## Built on by (most cited first, 6 of 6)

- [Integrative conformal p-values for powerful out-of-distribution testing with labeled outliers](https://mlsanity.com/paper/2208.11111/) (2022)
- [A Locally Adaptive Algorithm for Multiple Testing with Network Structure](https://mlsanity.com/paper/2203.11461/) (2022)
- [A New Perspective On Denoising Based On Optimal Transport](https://mlsanity.com/paper/2312.08135/) (2023)
- [AdaPT-GMM: Powerful and robust covariate-assisted multiple testing](https://mlsanity.com/paper/2106.15812/) (2021)
- [Empirical Bayes Estimation with Side Information: A Nonparametric Integrative Tweedie Approach](https://mlsanity.com/paper/2308.05883/) (2023)
- [Statistical Learning for Heterogeneous Treatment Effects: Pretraining, Prognosis, and Prediction](https://mlsanity.com/paper/2505.00310/) (2025)

## Abstract (arXiv)

A fundamental task in the analysis of datasets with many variables is screening for associations. This can be cast as a multiple testing task, where the objective is achieving high detection power while controlling type I error. We consider m hypothesis tests represented by pairs ((Pi, Xi))1≤ i ≤ m of p-values Pi and covariates Xi, such that Pi perp Xi if Hi is null. Here, we show how to use information potentially available in the covariates about heterogeneities among hypotheses to increase power compared to conventional procedures that only use the Pi. To this end, we upgrade existing weighted multiple testing procedures through the Independent Hypothesis Weighting (IHW) framework to use data-driven weights that are calculated as a function of the covariates. Finite sample guarantees, e.g., false discovery rate (FDR) control, are derived from cross-weighting, a data-splitting approach that enables learning the weight-covariate function without overfitting as long as the hypotheses can be partitioned into independent folds, with arbitrary within-fold dependence. IHW has increased power compared to methods that do not use covariate information. A key implication of IHW is that hypothesis rejection in common multiple testing setups should not proceed according to the ranking of the p-values, but by an alternative ranking implied by the covariate-weighted p-values.