64 papers with code • 0 benchmarks • 0 datasets
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We introduce new inference procedures for counterfactual and synthetic control methods for policy evaluation.
Convolutional image classifiers can achieve high predictive accuracy, but quantifying their uncertainty remains an unresolved challenge, hindering their deployment in consequential settings.
We develop a method to construct distribution-free prediction intervals for dynamic time-series, called \Verb|EnbPI| that wraps around any bootstrap ensemble estimator to construct sequential prediction intervals.
Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems.
This paper introduces libconform v0. 1. 0, a Python library for the conformal prediction framework, licensed under the MIT-license.