Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling
Institute for Data, Systems, and Society, Massachusetts Institute of Technology（数据、系统与社会研究所，麻省理工学院）；Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology（电气工程与计算机科学系，麻省理工学院）；Stanford Data Science and Department of Statistics, Stanford University（斯坦福大学数据科学与统计学系）
Dan M. Kluger, Kerri Lu, Tijana Zrnic, Sherrie Wang, Stephen Bates
Machine learning models are increasingly used to produce predictions that serve as input data in subsequent statistical analyses. For example, computer vision predictions of economic and environmental indicators based on satellite imagery are used in downstream regressions; similarly, language models are widely used to approximate human ratings and opinions in social science research. However, failure to properly account for errors in the machine learning predictions renders standard statistical procedures invalid. Prior work uses what we call the Predict-Then-Debias estimator to give valid confidence intervals when machine learning algorithms impute missing variables, assuming a small complete sample from the population of interest. We expand the scope by introducing bootstrap confidence intervals that apply when the complete data is a nonuniform (i.e., weighted, stratified, or clustered) sample and to settings where an arbitrary subset of features is imputed. Importantly, the method can be applied to many settings without requiring additional calculations. We prove that these confidence intervals are valid under no assumptions on the quality of the machine learning model and are no wider than the intervals obtained by methods that do not use machine learning predictions.
Electricité De France R&D（法国电力公司研发部）；INRIA（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；École polytechnique（巴黎综合理工学院）；Institut Polytechnique de Paris（巴黎理工学院）；Technion - Israel Institute of Technology（以色列理工学院）
