A Note on the Prediction-Powered Bootstrap

2024-05-28

arXiv.org

AI Summary

Traditional asymptotic theory (e.g., the Central Limit Theorem) often fails for complex research problems, and prediction-driven inference lacks flexible, assumption-light tools. Method: We propose PPBoot—a novel method that directly embeds standard bootstrap resampling into the Prediction-Powered Inference (PPI) framework, eliminating reliance on asymptotic normality assumptions. PPBoot integrates arbitrary black-box prediction models with bootstrap-based uncertainty quantification and incorporates bias correction to enable valid statistical inference for arbitrary estimators. Contribution/Results: Compared to CLT-dependent PPI(++) methods, PPBoot achieves comparable or superior performance in multivariate regression and causal effect estimation. Crucially, it remains robust in settings where the CLT breaks down—such as small samples and non-i.i.d. data—while maintaining conceptual simplicity and ease of implementation. PPBoot thus substantially broadens the applicability and reliability of prediction-powered inference.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Probabilistic Inference
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Predictive Research
• Complex Problems
• Mathematical Theory

Innovation

• PPBoot method
• prediction-driven bootstrapping
• complex problem applicability