Work

Prediction de‐correlated inference: A safe approach for post‐prediction inference

Year: 2024

Type: article

Abstract: Summary In modern data analysis, it is common to use machine learning methods to predict outcomes on unlabelled datasets and then use these pseudo‐outcomes in subsequent statistical inference. Inference in this setting is often called post‐prediction inference. We propose a novel assumption‐lean framework for statistical inference under post‐prediction setting, called prediction de‐correlated inference (PDC). Our approach is safe, in the sense that PDC can automatically adapt to any black‐box machine‐learning model and consistently outperform the supervised counterparts. The PDC framework also offers easy extensibility for accommodating multiple predictive models. Both numerical results and real‐world data analysis demonstrate the superiority of PDC over the state‐of‐the‐art methods.

Source: Australian & New Zealand Journal of Statistics

DOI: 10.1111/anzs.12429

Authors: Feng Gan, Wanfeng Liang, Changliang Zou

Institutions: Nankai University, Dongbei University of Finance and Economics

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China