Conformal prediction intervals for the individual treatment effect

2020/06/02 by Kivaranovic, Danijel, Ristl, Robin, Posch, Martin +1 · 1 citation
#FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.2006.01474

Abstract

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.

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• Prediction Intervals for Individual Treatment Effects in a Multiple Decision Point Framework using Conformal Inference

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