# Conformal prediction intervals for the individual treatment effect

@article{Kivaranovic2020ConformalPI, title={Conformal prediction intervals for the individual treatment effect}, author={Danijel Kivaranovic and Robin Ristl and Martin Posch and Hannes Leeb}, journal={arXiv: Methodology}, year={2020} }

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…

## 5 Citations

Conformal inference of counterfactuals and individual treatment effects

- Mathematics, Computer ScienceJournal of the Royal Statistical Society: Series B (Statistical Methodology)
- 2021

This work proposes a conformal inference-based approach that can produce reliable interval estimates for counterfactuals and individual treatment effects under the potential outcome framework and achieves the desired coverage with reasonably short intervals.

Adaptive Conformal Inference Under Distribution Shift

- Mathematics, Computer Science
- 2021

This work develops methods for forming prediction sets in an online setting where the data generating distribution is allowed to vary over time in an unknown fashion and achieves the desired coverage frequency over long-time intervals irrespective of the true data generating process.

Conformal Sensitivity Analysis for Individual Treatment Effects

- Mathematics
- 2021

Estimating an individual treatment effect (ITE) is essential to personalized decision making. However, existing methods for estimating the ITE often rely on unconfoundedness, an assumption that is…

Distributional conformal prediction.

- Economics, MathematicsProceedings of the National Academy of Sciences of the United States of America
- 2021

A robust method based on models for conditional distributions such as quantile and distribution regression that exploits the probability integral transform and relies on permuting estimated ranks to construct conditionally valid prediction intervals under heteroskedasticity.

AutoCP: Automated Pipelines for Accurate Prediction Intervals

- Computer Science
- 2020

Unlike the familiar AutoML frameworks that attempt to select the best prediction model, AutoCP constructs prediction intervals that achieve the user-specified target coverage rate while optimizing the interval length to be accurate and less conservative.

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