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Individualized Conformal

Author

Listed:
  • Fernando Delbianco

    (Universidad Nacional del Sur/CONICET)

  • Fernando Tohmé

    (Universidad Nacional del Sur/CONICET)

Abstract

The problem of individualized prediction can be addressed using variants of conformal prediction, obtaining the intervals to which the actual values of the variables of interest belong. Here we present a method based on detecting the observations that may be relevant for a given question and then using simulated controls to yield the intervals for the predicted values. This method is shown to be adaptive and able to detect the presence of latent relevant variables.

Suggested Citation

  • Fernando Delbianco & Fernando Tohmé, 2023. "Individualized Conformal," Working Papers 247, Red Nacional de Investigadores en Economía (RedNIE).
    • Handle: RePEc:aoz:wpaper:247
    as

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    File URL: https://rednie.eco.unc.edu.ar/files/DT/247.pdf
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    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.
    3. Jing Lei & Max G’Sell & Alessandro Rinaldo & Ryan J. Tibshirani & Larry Wasserman, 2018. "Distribution-Free Predictive Inference for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1094-1111, July.
    4. Min-ge Xie & Kesar Singh, 2013. "Confidence Distribution, the Frequentist Distribution Estimator of a Parameter: A Review," International Statistical Review, International Statistical Institute, vol. 81(1), pages 3-39, April.
    5. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    6. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Dec 2017.
    7. Xinran Li & Xiao-Li Meng, 2021. "A Multi-resolution Theory for Approximating Infinite-p-Zero-n: Transitional Inference, Individualized Predictions, and a World Without Bias-Variance Tradeoff," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(533), pages 353-367, January.
    8. Jieli Shen & Regina Y. Liu & Min-ge Xie, 2020. "iFusion: Individualized Fusion Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1251-1267, July.
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    More about this item

    Keywords

    Conformal Prediction; Individualized Inference; Split and Jacknife Distribution-Free Inference.;
    All these keywords.

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