# Unified Conformalized Multiple Testing with Full Data Efficiency

arXiv: https://arxiv.org/abs/2508.12085 · HTML: https://mlsanity.com/paper/2508.12085/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2025.

## Built on by (most cited first, 1 of 1)

- [Denoised Conformal Alignment for Reliable Selection of Conditional Average Treatment Effect Predictions](https://mlsanity.com/paper/2607.03161/) (2026)

## Abstract (arXiv)

Conformalized multiple testing offers a model-free way to control predictive uncertainty in decision-making. Existing methods typically use only part of the available data to build score functions tailored to specific settings. We propose a unified framework that puts data utilisation at the centre: it uses all available data-null, alternative, and unlabelled-to construct scores and calibrate p-values through a full permutation strategy. This unified use of all available data significantly improves power by enhancing non-conformity score quality and maximising calibration set size while rigorously controlling the false discovery rate. Crucially, our framework provides a systematic design principle for conformal testing and enables automatic selection of the best conformal procedure among candidates without extra data splitting. Extensive numerical experiments demonstrate that our enhanced methods deliver superior efficiency and adaptability across diverse scenarios.