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Updated: Jul 4, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

Prediction sets adaptive to unknown covariate shift.

Hongxiang Qiu¹, Edgar Dobriban¹, Eric Tchetgen Tchetgen¹

¹Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA.

Journal of the Royal Statistical Society. Series B, Statistical Methodology

|February 5, 2024

Summary

This summary is machine-generated.

This study introduces PredSet-1Step, a new method for creating reliable prediction sets that account for unknown covariate shift in statistical learning. It ensures accurate uncertainty quantification for better predictive modeling.

Keywords:

PAC guarantee covariate shift machine learning nonparametric inference nonparametric model prediction set

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Area of Science:

Statistical Learning
Uncertainty Quantification

Background:

Predicting sets of outcomes is key for uncertainty quantification in statistical learning.
Adapting prediction sets to unknown covariate shift remains a significant challenge.

Purpose of the Study:

To address the limitations of existing methods for constructing prediction sets under unknown covariate shift.
To propose a novel, flexible, distribution-free method for constructing prediction sets with statistical guarantees.

Main Methods:

Developed PredSet-1Step, a novel distribution-free method for constructing prediction sets.
The method provides an asymptotic coverage guarantee under unknown covariate shift.
Theoretical analysis shows the method is asymptotically probably approximately correct (APAC).

Main Results:

Demonstrated that prediction sets with finite-sample coverage guarantees can be uninformative.
PredSet-1Step achieves well-calibrated coverage error with high confidence for large samples.
Empirical validation in experiments and an HIV risk prediction dataset confirmed nominal coverage.

Conclusions:

PredSet-1Step offers an efficient and reliable approach for constructing prediction sets under unknown covariate shift.
The method enhances uncertainty quantification in statistical learning.
The findings have implications for various applications, including public health risk prediction.