Prediction De-Correlated Inference: A safe approach for post-prediction inference

• URL: http://arxiv.org/abs/2312.06478v3
• Date: Fri, 24 May 2024 00:02:16 GMT
• Title: Prediction De-Correlated Inference: A safe approach for post-prediction inference
• Authors: Feng Gan, Wanfeng Liang, Changliang Zou,
• Abstract summary: 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.
• Score: 0.8192907805418583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In modern data analysis, it is common to use machine learning methods to predict outcomes on unlabeled 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.

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