A (tight) upper bound for the length of confidence intervals with conditional coverage

Abstract

We show that two popular selective inference procedures, namely data carving (Fithian et al., 2017) and selection with a randomized response (Tian and Taylor, 2018), when combined with the polyhedral method (Lee et al., 2016), result in confidence intervals whose length is bounded. This contrasts results for confidence intervals based on the polyhedral method alone, whose expected length is typically infinite (Kivaranovic and Leeb, 2021). Moreover, we show that these two procedures always dominate corresponding sample-splitting methods in terms of interval length.