We introduce PPBoot: a bootstrap-based method for prediction-powered inference. PPBoot is applicable to arbitrary estimation problems and is very simple to implement, essentially only requiring one application of the bootstrap. Through a series of examples, we demonstrate that PPBoot often performs nearly identically to (and sometimes better than) the earlier PPI(++) method based on asymptotic normality$\unicode{x2013}$when the latter is applicable$\unicode{x2013}$without requiring any asymptotic characterizations. Given its versatility, PPBoot could simplify and expand the scope of application of prediction-powered inference to problems where central limit theorems are hard to prove.

翻译：本文提出PPBoot：一种基于引导法的预测驱动推断方法。PPBoot适用于任意估计问题，实现极为简单，本质上仅需执行一次引导过程。通过系列示例，我们证明当基于渐近正态性的早期PPI(++)方法适用时，PPBoot常表现出近乎相同（有时更优）的性能$\unicode{x2013}$且无需任何渐近特性分析。鉴于其普适性，PPBoot可简化和拓展预测驱动推断的应用范围，使其适用于中心极限定理难以证明的各类问题。