ExplainerPFN: Towards tabular foundation models for model-free zero-shot feature importance estimations

ArXi:2601.23068v2 Announce Type: replace-cross Computing the importance of features in supervised classification tasks is critical for model interpretability. Shapley values are a widely used approach for explaining model predictions, but require direct access to the underlying model, an assumption frequently violated in real-world deployments. We investigate whether meaningful feature attributions can be obtained in a zero-shot setting, using only the input data distribution and no evaluations of the target model.