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New method Calibrated Prediction-Powered Inference enhances semisupervised estimation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced Calibrated Prediction-Powered Inference (CPPI), a novel method for semisupervised mean estimation. CPPI enhances existing techniques like augmented inverse-probability weighting (AIPW) by post-hoc calibrating prediction scores on a labeled sample before applying them to semisupervised estimation. This calibration step, which can be linear or isotonic, aims to improve both prediction accuracy and the efficiency of the resulting estimators. The method has demonstrated competitive or superior performance compared to AIPW and other prediction-powered inference methods in simulations and real-world experiments. AI

RANK_REASON The cluster contains a research paper detailing a new statistical inference method. [lever_c_demoted from research: ic=1 ai=0.7]

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New method Calibrated Prediction-Powered Inference enhances semisupervised estimation

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The cluster contains a research paper detailing a new statistical inference method. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Lars van der Laan, Mark Van Der Laan ·

    Calibeating Prediction-Powered Inference

    arXiv:2604.21260v2 Announce Type: replace-cross Abstract: We study semisupervised mean estimation with a small labeled sample, a large unlabeled sample, and a black-box prediction model whose output may be miscalibrated. A standard approach in this setting is augmented inverse-pr…

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