  Machine and deep learning survival models demonstrate similar or even
improved time-to-event prediction capabilities compared to classical
statistical learning methods yet are too complex to be interpreted by humans.
Several model-agnostic explanations are available to overcome this issue;
however, none directly explain the survival function prediction. In this paper,
we introduce SurvSHAP(t), the first time-dependent explanation that allows for
interpreting survival black-box models. It is based on SHapley Additive
exPlanations with solid theoretical foundations and a broad adoption among
machine learning practitioners. The proposed methods aim to enhance precision
diagnostics and support domain experts in making decisions. Experiments on
synthetic and medical data confirm that SurvSHAP(t) can detect variables with a
time-dependent effect, and its aggregation is a better determinant of the
importance of variables for a prediction than SurvLIME. SurvSHAP(t) is
model-agnostic and can be applied to all models with functional output. We
provide an accessible implementation of time-dependent explanations in Python
at http://github.com/MI2DataLab/survshap.
