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Testing for Outliers with Conformal p-values

We study the construction of p-values for nonparametric outlier detection, taking a multiple-testing perspective. The framework is that of conformal prediction, which wraps around any machine-learning algorithm to provide finite-sample guarantees regarding the validity of predictions for future testpoints. In this setting, existing methods can compute p-values that are marginally valid but mutually dependent for different future test points.

This repository contains a software implementation and guided examples for the methodology developed in the accompanying paper, which provides a new method to compute p-values that are both conditionally valid and independent of each other for different future test points, thus allowing multiple testing with stronger stronger type-I error guarantees.

"Testing for Outliers with Conformal p-values"
Stephen Bates, Emmanuel Candes, Lihua Lei, Yaniv Romano, and Matteo Sesia. 
accepted in Annals of Statistics (2022)
arXiv pre-print: https://arxiv.org/abs/2104.08279