Literature DB >> 33517401

Invited Commentary: Quantitative Bias Analysis can see the Forest for the Trees.

Paul Gustafson1.   

Abstract

The article by Jiang et al (Am J. Epidemiol.) extends quantitative bias analysis from the realm of statistical models to the realm of machine learning algorithms. Given the rooting of statistical models in the spirit of explanation and the rooting of machine learning algorithms in the spirt of prediction, this extension is thought provoking indeed. Some such thoughts are expounded here.
© The Author(s) 2021. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  machine learning; measurement error; misclassification; quantitative bias analysis; random forests

Year:  2021        PMID: 33517401     DOI: 10.1093/aje/kwab011

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  1 in total

1.  Jiang et al. Respond to "Quantitative Bias Analysis".

Authors:  Tammy Jiang; Jaimie L Gradus; Timothy L Lash; Matthew P Fox
Journal:  Am J Epidemiol       Date:  2021-09-01       Impact factor: 4.897

  1 in total

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