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Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron

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  • Jerome Friedman
  • Trevor Hastie
  • Robert Tibshirani

Abstract

Professor Efron has presented us with a thought‐provoking paper on the relationship between prediction, estimation, and attribution in the modern era of data science. While we appreciate many of his arguments, we see more of a continuum between the old and new methodology, and the opportunity for both to improve through their synergy.

Suggested Citation

  • Jerome Friedman & Trevor Hastie & Robert Tibshirani, 2020. "Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron," International Statistical Review, International Statistical Institute, vol. 88(S1), pages 73-74, December.
    • Handle: RePEc:bla:istatr:v:88:y:2020:i:s1:p:s73-s74
    DOI: 10.1111/insr.12414
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    References listed on IDEAS

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    1. Sebastian Lapuschkin & Stephan Wäldchen & Alexander Binder & Grégoire Montavon & Wojciech Samek & Klaus-Robert Müller, 2019. "Unmasking Clever Hans predictors and assessing what machines really learn," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
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