  The Shapley value concept from cooperative game theory has become a popular
technique for interpreting ML models, but efficiently estimating these values
remains challenging, particularly in the model-agnostic setting. Here, we
revisit the idea of estimating Shapley values via linear regression to
understand and improve upon this approach. By analyzing the original KernelSHAP
alongside a newly proposed unbiased version, we develop techniques to detect
its convergence and calculate uncertainty estimates. We also find that the
original version incurs a negligible increase in bias in exchange for
significantly lower variance, and we propose a variance reduction technique
that further accelerates the convergence of both estimators. Finally, we
develop a version of KernelSHAP for stochastic cooperative games that yields
fast new estimators for two global explanation methods.
