An Exact Game-Theoretic Variable Importance Index for Generalized Additive Models
提出基于Shapley值的方差分配方法，为广义加性模型导出变量重要性的闭式表达式，可处理高维数据和任意依赖结构，比p值提供更丰富的变量排序信息。
Generalized Additive Models (GAMs) are widely used in statistics. In this work, we aim to tackle the challenge of identifying the most influential variables in GAMs. To accomplish this, we introduce a variance allocation approach based on the Shapley value. We derive a closed-form expression for this importance index, which allows for its computation on high-dimensional datasets and with any dependence structure. We discuss the practical implication that when a variable’s importance is negligible, it can be safely eliminated from the GAM, simplifying the model. Through our case studies, we demonstrate that Shapley values offer more informative insights than p-values in terms of ranking the importance of variables. All the code is available online in the supplementary material.