  When data are right-censored, i.e. some outcomes are missing due to a limited
period of observation, survival analysis can compute the "time to event".
Multiple classes of outcomes lead to a classification variant: predicting the
most likely event, known as competing risks, which has been less studied. To
build a loss that estimates outcome probabilities for such settings, we
introduce a strictly proper censoring-adjusted separable scoring rule that can
be optimized on a subpart of the data because the evaluation is made
independently of observations. It enables stochastic optimization for competing
risks which we use to train gradient boosting trees. Compared to 11
state-of-the-art models, this model, MultiIncidence, performs best in
estimating the probability of outcomes in survival and competing risks. It can
predict at any time horizon and is much faster than existing alternatives.
