Reliability-oriented sensitivity analysis methods have been developed for understanding the influence of model inputs relative to events which characterize the failure of a system (e.g., a threshold exceedance of the model output). In this field, the target sensitivity analysis focuses primarily on capturing the influence of the inputs on the occurrence of such a critical event. This paper proposes new target sensitivity indices, based on the Shapley values and called “target Shapley effects”, allowing for interpretable sensitivity measures under dependent inputs. Two algorithms (one based on Monte Carlo sampling, and a given-data algorithm based on a nearest-neighbors procedure) are proposed for the estimation of these target Shapley effects based on the ℓ² norm. Additionally, the behavior of these target Shapley effects are theoretically and empirically studied through various toy-cases. Finally, the application of these new indices in two real-world use-cases (a river flood model and a COVID-19 epidemiological model) is discussed.

已经开发了面向可靠性的敏感性分析方法来理解模型输入相对于表征系统故障的事件的影响（例如，模型输出的阈值超出）。在该领域，目标敏感性分析主要侧重于捕捉输入对此类关键事件发生的影响。本文基于 Shapley 值提出了新的目标敏感度指数，称为“目标 Shapley 效应”，允许在相关输入下进行可解释的敏感度测量。提出了两种算法（一种基于蒙特卡罗采样，一种基于最近邻过程的给定数据算法）用于基于ℓ ²来估计这些目标 Shapley 效应规范。此外，通过各种玩具箱对这些目标沙普利效​​应的行为进行了理论和实证研究。最后，讨论了这些新指数在两个实际用例（河流洪水模型和 COVID-19 流行病学模型）中的应用。