  Scoring rules are used to evaluate the quality of predictions that take the
form of probability distributions. A scoring rule is strictly proper if its
expected value is uniquely minimized by the true probability distribution. One
of the most well-known and widely used strictly proper scoring rules is the
logarithmic scoring rule. We propose a version of the logarithmic scoring rule
for competing risks data and show that it remains strictly proper under
non-informative censoring.
