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A structural characterization of shortcut features for prediction.
- Source :
-
European journal of epidemiology [Eur J Epidemiol] 2022 Jun; Vol. 37 (6), pp. 563-568. Date of Electronic Publication: 2022 Jul 06. - Publication Year :
- 2022
-
Abstract
- With the rising use of machine learning for healthcare applications, practitioners are increasingly confronted with the limitations of prediction models that are trained in one setting but meant to be deployed in several others. One recently identified limitation is so-called shortcut learning, whereby a model learns to associate features with the prediction target that do not maintain their relationship across settings. Famously, the watermark on chest x-rays has been demonstrated to be an instance of a shortcut feature. In this viewpoint, we attempt to give a structural characterization of shortcut features in terms of causal DAGs. This is the first attempt at defining shortcut features in terms of their causal relationship with a model's prediction target.<br /> (© 2022. Springer Nature B.V.)
- Subjects :
- Causality
Humans
Machine Learning
Subjects
Details
- Language :
- English
- ISSN :
- 1573-7284
- Volume :
- 37
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- European journal of epidemiology
- Publication Type :
- Academic Journal
- Accession number :
- 35792990
- Full Text :
- https://doi.org/10.1007/s10654-022-00892-3