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.

翻译：本文提出φ-检验——一种结合Shapley归因与选择性推断的黑盒预测器全局特征选择及显著性检验方法。给定训练完成的模型与评估数据集，φ-检验通过SHAP引导的筛选机制执行特征初选，并基于具有可处理选择性推断形式的选取规则，在筛选后的特征上拟合线性替代模型。对于每个保留的特征，该方法在全局特征重要性表中输出基于Shapley的全局评分、替代模型系数，以及选择后p值与置信区间。在基于树模型与神经网络架构的真实表格数据回归任务上的实验表明，φ-检验在仅使用少量特征的情况下，仍能保持原始模型的大部分预测能力，且生成的特征集在重抽样与不同骨干模型类别间保持较高稳定性。在此类场景中，φ-检验可作为实用的全局解释层，将基于Shapley的重要性度量与经典统计推断相连接。