Stabilizing Estimates of Shapley Values with Control Variates

Jeremy Goldwasser, Giles Hooker

Abstract: Shapley values are among the most popular tools for explaining predictions of black-box machine learning models. However, their high computational cost motivates the use of sampling approximations, inducing a considerable degree of uncertainty. To stabilize these model explanations, we propose ControlSHAP, an approach based on the Monte Carlo technique of control variates. Our methodology is applicable to any machine learning model and requires virtually no extra computation or modeling effort. On several high-dimensional datasets, we find it can produce dramatic reductions in the variability of Shapley estimates.