  Reliability-oriented sensitivity analysis methods have been developed for
understanding the influence of model inputs relatively to events characterizing
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 influence measures of each
input in the case of dependence between the inputs. Two algorithms (a Monte
Carlo sampling one, and a given-data algorithm) 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, applications on two realistic
use-cases (a river flood model and a COVID-19 epidemiological model) are
discussed.
