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Conformal prediction intervals for the individual treatment effect
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We propose several prediction intervals procedures for the individual treatment effect with either finite-sample or asymptotic coverage guarantee in a non-parametric regression setting, where non-linear regression functions, heteroskedasticity and non-Gaussianity are allowed. The construct the prediction intervals we use the conformal method of Vovk et al. (2005). In extensive simulations, we compare the coverage probability and interval length of our prediction interval procedures. We demonstrate that complex learning algorithms, such as neural networks, can lead to narrower prediction intervals than simple algorithms, such as linear regression, if the sample size is large enough.
Forward citations
Cited by 2 Pith papers
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CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
CLEAR jointly calibrates aleatoric and epistemic uncertainty estimates with two parameters and produces narrower calibrated prediction intervals across 17 regression datasets.
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Local conformal prediction for individual causal effects
A local conformal prediction framework that aims to provide finite-sample valid intervals for individual causal effects by calibrating on causally similar units.
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