论文标题
沙普利曲线：平滑的视角
Shapley Curves: A Smoothing Perspective
论文作者
论文摘要
本文从非参数（或平滑）的角度来看，对沙普利价值的有限统计理解是一种可变的重要性度量。我们介绍了人口级\ textit {shapley曲线}，以测量由条件期望函数和协变量分布确定的真正变量重要性。在定义了估计和估计之后，我们在两种领先的估计策略的一般条件下得出了最小值收敛速率和渐近态性。对于有限的样本推断，我们提出了一种新的版本的野生引导程序，该过程量身定制，用于在沙普利曲线的估计中捕获较低的术语。数值研究证实了我们的理论发现，经验应用分析了车辆价格的决定因素。
This paper fills the limited statistical understanding of Shapley values as a variable importance measure from a nonparametric (or smoothing) perspective. We introduce population-level \textit{Shapley curves} to measure the true variable importance, determined by the conditional expectation function and the distribution of covariates. Having defined the estimand, we derive minimax convergence rates and asymptotic normality under general conditions for the two leading estimation strategies. For finite sample inference, we propose a novel version of the wild bootstrap procedure tailored for capturing lower-order terms in the estimation of Shapley curves. Numerical studies confirm our theoretical findings, and an empirical application analyzes the determining factors of vehicle prices.
