Distributional conformal prediction
Name
pnas.2107794118.pdf
Description
Published version
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871.98 KB
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5cfbd7a559d6b105f87c7a2bb848f8ca
Author(s)
Chernozhukov, Victor
Wüthrich, Kaspar
Zhu, Yinchu
Date Issued
2021
Journal
Proceedings of the National Academy of Sciences of the United States of America
Publisher
Proceedings of the National Academy of Sciences
Citation
Chernozhukov, Victor, Wüthrich, Kaspar and Zhu, Yinchu. 2021. "Distributional conformal prediction." Proceedings of the National Academy of Sciences of the United States of America, 118 (48).
Version
Final published version
Abstract
Significance
Prediction problems are important in many contexts. Examples include cross-sectional prediction, time series forecasting, counterfactual prediction and synthetic controls, and individual treatment effect prediction. We develop a prediction method that works in conjunction with many powerful classical methods (e.g., conventional quantile regression) as well as modern high-dimensional methods for estimating conditional distributions (e.g., quantile neural networks). Unlike many existing prediction approaches, our method is valid conditional on the observed predictors and efficient under some conditions. Importantly, our method is also robust; it exhibits unconditional coverage guarantees under model misspecification, under overfitting, and with time series data.
Prediction problems are important in many contexts. Examples include cross-sectional prediction, time series forecasting, counterfactual prediction and synthetic controls, and individual treatment effect prediction. We develop a prediction method that works in conjunction with many powerful classical methods (e.g., conventional quantile regression) as well as modern high-dimensional methods for estimating conditional distributions (e.g., quantile neural networks). Unlike many existing prediction approaches, our method is valid conditional on the observed predictors and efficient under some conditions. Importantly, our method is also robust; it exhibits unconditional coverage guarantees under model misspecification, under overfitting, and with time series data.
MIT Department
Massachusetts Institute of Technology. Department of Economics
Statistics and Data Science Center (Massachusetts Institute of Technology)
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1073/PNAS.2107794118
