  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 $\ell^2$ 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.
