Prediction-Powered Inference Pdf

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Angelopoulos et al. [1] develop prediction-powered inference (PPI) to allow estimation and inference using black-box machine learning models; our work’s debt to the original proposal . Recent work has focused on the very common practice of prediction-based inference: that is, (i) using a pre-trained machine learning model to predict an unobserved response variable, and . Nov 2,  · This work introduces PoSt-Prediction Adaptive inference (PSPA) that allows valid and powerful inference based on ML-predicted data, and guarantees reliable statistical . Our main purpose here is a brief discussion of asymptotic efficiency in Prediction-Powered Inference (PPI) [2] and its improved variant PPI++ [1]. To do so, we make connections with the semipara-metric efficiency literature, and we address a few challenges around inference with missing data. Recent work has focused on the very common practice of prediction-based inference: that is, (i) using a pre-trained machine learning model to predict an unobserved response variable, and then (ii) conducting inference on the association between that predicted response and some covariates. Prediction-powered inference leads to powerful and provably valid confidence intervals and p-values for a broad class of statistical problems, enabling researchers to reliably incorporate machine learning into their analyses. We provide practical algorithms for constructing prediction-powered confidence intervals for.