How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features
In clinical prediction settings the importance of a high-dimensional feature like genomics is often assessed by evaluating the change in predictive performance when adding it to a set of traditional clinical variables. This approach is questionable, because it does not account for collinearity nor known directionality of dependencies between variables. We suggest to use asymmetric Shapley values as a more suitable alternative to quantify feature importance in the context of a mixed-dimensional prediction model. We focus on a setting that is particularly relevant in clinical prediction: disease state as a mediating variable for genomic effects, with additional confounders for which the dir...
| Authors | Mark A. van de Wiel, Jeroen Goedhart, Martin Jullum et al. |
|---|---|
| Primary category | stat.ML |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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