This repository contains the codes for the paper "Shapley Values: A Smoothing Perspective".
Originating from cooperative game theory, Shapley values have become one of the most widely used measures for variable importance in applied Machine Learning. However, the statistical understanding of Shapley values is still limited. In this paper we take a nonparametric (or smoothing) perspective by proposing Shapley curves as a local measure for variable importance. We propose two estimation strategies and derive the consistency and asymptotic normality both under independence and dependence among the features. This allows us to construct confidence intervals in order to conduct inference on the estimated Shapley curves. In an empirical application, we analyze the attributes which drive the prices of cars.