# Prediction-Powered Inference

**Authors:** Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, Tijana Zrnic  
**arXiv:** [2301.09633](https://arxiv.org/abs/2301.09633)  
**Date:** 2023-01-23  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2301.09633v4/

## Key points

- Prediction-powered inference is a groundbreaking framework developed by Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, and Tijana Zrnic.
- This innovative approach revolutionizes statistical inference by incorporating machine learning predictions into experimental datasets.
- The framework provides simple algorithms for calculating confidence intervals for means, quantiles, and regression coefficients without restrictions on the underlying machine-learning algorithm.
- Prediction-powered inference produces smaller confidence intervals with more accurate predictions, enhancing efficiency and accuracy in drawing conclusions from data.
- It is valuable across diverse fields such as proteomics, astronomy, genomics, remote sensing, census analysis, and ecology.
- The framework leverages machine learning to advance data analysis processes beyond traditional statistical methods.
- Researchers can draw valid conclusions more efficiently using machine learning techniques with this framework.
- The authors have made their code accessible through GitHub (https://github.com/aangelopoulos/ppi_py) for other researchers to implement and benefit from this methodology.

## Summary

Prediction-powered inference is a groundbreaking framework developed by Anastasios N. Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I. Jordan, and Tijana Zrnic that revolutionizes statistical inference in the presence of machine learning predictions. This innovative approach allows researchers to make valid statistical inferences by incorporating predictions from a machine-learning system into an experimental dataset. The framework provides simple algorithms for calculating confidence intervals for various quantities such as means, quantiles, and regression coefficients without imposing any restrictions on the underlying machine-learning algorithm generating the predictions. One of the key advantages of prediction-powered inference is its ability to produce smaller confidence intervals with more accurate predictions. This feature enhances the efficiency and accuracy of drawing conclusions from data, making it a valuable tool for researchers across diverse fields such as proteomics, astronomy, genomics, remote sensing, census analysis, and ecology. The implications of prediction-powered inference extend beyond traditional statistical methods by leveraging the power of machine learning to enhance data analysis processes. By enabling researchers to draw valid conclusions more efficiently using machine learning techniques, this framework opens up new possibilities for advancing research in various scientific disciplines. Furthermore, the authors have made their code accessible through GitHub (https://github.com/aangelopoulos/ppi_py), allowing other researchers to implement and benefit from this cutting-edge methodology. Overall,Prediction-Powered Inference represents a significant advancement in statistical inference that has the potential to transform how researchers analyze and interpret data in complex research settings.