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arXiv · 2002.12024

Computing Shapley Effects for Sensitivity Analysis

Elmar Plischke·Giovanni Rabitti·Emanuele Borgonovo
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Abstract

Shapley effects are attracting increasing attention as sensitivity measures. When the value function is the conditional variance, they account for the individual and higher order effects of a model input. They are also well defined under model input dependence. However, one of the issues associated with their use is computational cost. We present a new algorithm that offers major improvements for the computation of Shapley effects, reducing computational burden by several orders of magnitude (from $k!\cdot k$ to $2^k$, where $k$ is the number of inputs) with respect to currently available implementations. The algorithm works in the presence of input dependencies. The algorithm also makes it possible to estimate all generalized (Shapley-Owen) effects for interactions.

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Elmar Plischke, Giovanni Rabitti, Emanuele Borgonovo. 2020-02-27. Computing Shapley Effects for Sensitivity Analysis. https://doi.org/10.1137/19m1304738

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