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Reliability-oriented sensitivity analysis methods have been developed forunderstanding the influence of model inputs relative to events whichcharacterize the failure of a system (e.g., a threshold exceedance of the modeloutput). In this field, the target sensitivity analysis focuses primarily oncapturing the influence of the inputs on the occurrence of such a criticalevent. This paper proposes new target sensitivity indices, based on the Shapleyvalues and called "target Shapley effects", allowing for interpretablesensitivity measures under dependent inputs. Two algorithms (one based on MonteCarlo sampling, and a given-data algorithm based on a nearest-neighborsprocedure) are proposed for the estimation of these target Shapley effectsbased on the $\ell^2$ norm. Additionally, the behavior of these target Shapleyeffects are theoretically and empirically studied through various toy-cases.Finally, the application of these new indices in two real-world use-cases (ariver flood model and a COVID-19 epidemiological model) is discussed.

Developments and applications of Shapley effects to reliability-oriented sensitivity analysis with correlated inputs