论文标题
计算Shapley效应以进行灵敏度分析
Computing Shapley Effects for Sensitivity Analysis
论文作者
论文摘要
随着灵敏度措施，沙普利效应引起了越来越多的关注。当值函数是条件差异时，它们会解释模型输入的个体和高阶效应。它们在模型输入依赖性下也得到很好的定义。但是，与其使用相关的问题之一是计算成本。我们提出了一种新的算法，可为Shapley效果的计算提供重大改进，从而减少了几个数量级（从$ k！\ cdot k $到$ 2^k $，其中$ k $是输入的数量），这是对当前可用实施的。该算法在有输入依赖性的情况下起作用。该算法还可以估算所有具有相互作用的广义（Shapley-Owen）效应。
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.
