On Shapley Value for Measuring Importance of Dependent Inputs
On Shapley Value for Measuring Importance of Dependent Inputs
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DOI：
10.1137/16m1097717
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
A. Owen;C. Prieur
中科院分区：
文献类型：
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作者：
A. Owen;C. Prieur
本文用Shapley值来量化随机输入变量对函数的重要性。当输入变量相互依赖时，基于ANOVA分解的备选方案可能会遇到概念和计算问题。我们在这里的主要目标是证明Shapley值消除了概念问题。我们通过一些简单的例子来实现这一点，其中Shapley值导致直观合理的近乎封闭的形式值。
This paper makes the case for using Shapley value to quantify the importance of random input variables to a function. Alternatives based on the ANOVA decomposition can run into conceptual and computational problems when the input variables are dependent. Our main goal here is to show that Shapley value removes the conceptual problems. We do this with some simple examples where Shapley value leads to intuitively reasonable nearly closed form values.