A Note on the Prediction-Powered Bootstrap

TL;DR

PPBoot is a simple, versatile bootstrap-based method for prediction-powered inference that performs comparably or better than existing methods without needing asymptotic normality, broadening its applicability.

Contribution

Introduces PPBoot, a straightforward bootstrap method for prediction-powered inference applicable to diverse estimation problems without relying on asymptotic normality.

Findings

PPBoot performs nearly identically to PPI(++) in various examples.

PPBoot often outperforms PPI(++) when applicable.

PPBoot simplifies prediction-powered inference, especially where CLTs are difficult.

Abstract

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.