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Unified Conformalized Multiple Testing with Full Data Efficiency

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📄 Abstract

Abstract: 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 utilization at the center: it uses all available data-null, alternative, and unlabeled-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 maximizing 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.

Key Contributions

This paper proposes a unified framework for conformalized multiple testing that achieves full data efficiency by using all available data (null, alternative, unlabeled). This approach significantly improves power and adaptability by enhancing score quality and maximizing calibration set size while rigorously controlling the false discovery rate.

Business Value

Provides a more powerful and data-efficient method for statistical inference and decision-making under uncertainty, applicable in various fields requiring rigorous hypothesis testing.

Paper Metadata

Innovation Type

Algorithmic Improvement

Deployment Feasibility

High, as it's a model-free statistical framework that can be integrated into existing data analysis pipelines.

Limitations Addressed

The partial data utilization in existing conformal multiple testing methods, which limits their power and adaptability. This work addresses it by using all available data.

Performance Gains

improved power,enhanced adaptability,superior efficiency

Technical Tags

conformalized multiple testingdata efficiencyfalse discovery ratepermutation strategymodel-freepredictive uncertaintystatistical inferencemachine learning

Research Topics

Multiple Hypothesis TestingConformal PredictionStatistical InferenceModel-Free MethodsData Efficiency in ML

Methods & Architectures

unified frameworkfull permutation strategynon-conformity score calibrationdata utilization

Applications & Tasks

Statistical Inference Machine Learning Decision Making Data Analysis Limited data utilization in existing conformal multiple testing methodsImproving power and adaptability of conformal testing Conformalized multiple testingFalse Discovery Rate (FDR) controlPredictive uncertainty quantification

Related Fields

StatisticsMachine LearningDecision TheoryData MiningHypothesis Testing

Keywords

conformal testingmultiple testingdata efficiencyfalse discovery ratemodel-freestatistical inferenceuncertainty quantificationpermutation testmachine learningdecision making

Academic Context

#Multiple Hypothesis Testing#Conformal Prediction#Statistical Inference#Model-Free Methods#Data Efficiency in ML

Commercial Potential

Potential Products

Statistical analysis softwareDecision support toolsUncertainty quantification libraries

Target Industries

TechnologyFinanceBiotechnologyPharmaceuticalsResearch

Use Case Examples

Identifying significant features in high-dimensional dataControlling false positives in A/B testingMaking reliable decisions under uncertainty in scientific experiments

Competitive Edge

Offers a unified and more data-efficient approach to conformalized multiple testing compared to existing methods that use partial data.

Market Opportunity

Broad applicability in data analysis and statistical modeling.

Revenue Models

Software licensingconsulting services.

Resource Requirements

Compute Needs

Moderate (for permutation strategy)

Data Requirements

Labeled and unlabeled data for calibration and testing.

Deployment Constraints

Computational cost of permutation strategy can be high for very large datasets.

Scalability

Scalability depends on the efficiency of the permutation strategy; potential for parallelization.

Production Readiness

Maturity Level

Research

Time to Market

Medium (requires implementation and validation)

Patent Potential

Medium (unified framework design)

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