Fast approximate estimation of conditional Shapley values when using a linear explainer

In this paper, we develop three new methods, two approximate and one exact, for fast estimation of conditional Shapley values when a linear regression model is used as the explainer.

We apply constrained Gaussian Markov Random Field theory and sparse matrix algebra packages to estimate the coefficients of all submodels jointly and not sequentially.

In the numerical case studies, our methods match the accuracy of the Sequential method in the shapr R-package while reducing the computation time from hours to minutes or seconds.

Compared to the Iterative method in shapr, which samples coalitions until a convergence criterion is met, our methods estimate all $2^p$ coalitions when there are $p$ predictors present.

When the iterative method requires all or a large fraction of the coalitions, our methods are substantially faster and with equal or better accuracy.

When few coalitions suffice and the shapr iterative method is very fast, our methods deliver enumeration of all submodels, and in one case study over two million models are estimated within minutes.

We recommend the exact method, since it has no tuning parameters and the three methods give comparable results.