Selection by Prediction with Conformal p-values

• URL: http://arxiv.org/abs/2210.01408v3
• Date: Sat, 27 May 2023 01:24:14 GMT
• Title: Selection by Prediction with Conformal p-values
• Authors: Ying Jin, Emmanuel J. Cand\`es
• Abstract summary: We study screening procedures that aim to select candidates whose unobserved outcomes exceed user-specified values. We develop a method that wraps around any prediction model to produce a subset of candidates while controlling the proportion of falsely selected units.
• Score: 7.917044695538599
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Decision making or scientific discovery pipelines such as job hiring and drug discovery often involve multiple stages: before any resource-intensive step, there is often an initial screening that uses predictions from a machine learning model to shortlist a few candidates from a large pool. We study screening procedures that aim to select candidates whose unobserved outcomes exceed user-specified values. We develop a method that wraps around any prediction model to produce a subset of candidates while controlling the proportion of falsely selected units. Building upon the conformal inference framework, our method first constructs p-values that quantify the statistical evidence for large outcomes; it then determines the shortlist by comparing the p-values to a threshold introduced in the multiple testing literature. In many cases, the procedure selects candidates whose predictions are above a data-dependent threshold. Our theoretical guarantee holds under mild exchangeability conditions on the samples, generalizing existing results on multiple conformal p-values. We demonstrate the empirical performance of our method via simulations, and apply it to job hiring and drug discovery datasets.

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