$φ$-test: Global Feature Selection and Inference for Shapley Additive Explanations

• URL: http://arxiv.org/abs/2512.07578v1
• Date: Mon, 08 Dec 2025 14:14:01 GMT
• Title: $φ$-test: Global Feature Selection and Inference for Shapley Additive Explanations
• Authors: Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool, Gisung Oh,
• Abstract summary: $$-test is a global feature-selection and significance procedure for black-box predictors.<n>$$-test acts as a practical global explanation layer linking Shapley-based importance summaries with classical statistical inference.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose $φ$-test, a global feature-selection and significance procedure for black-box predictors that combines Shapley attributions with selective inference. Given a trained model and an evaluation dataset, $φ$-test performs SHAP-guided screening and fits a linear surrogate on the screened features via a selection rule with a tractable selective-inference form. For each retained feature, it outputs a Shapley-based global score, a surrogate coefficient, and post-selection $p$-values and confidence intervals in a global feature-importance table. Experiments on real tabular regression tasks with tree-based and neural backbones suggest that $φ$-test can retain much of the predictive ability of the original model while using only a few features and producing feature sets that remain fairly stable across resamples and backbone classes. In these settings, $φ$-test acts as a practical global explanation layer linking Shapley-based importance summaries with classical statistical inference.

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