Reviews: Multitask Boosting for Survival Analysis with Competing Risks

The paper tackles a timely area of research, namely new approaches for time-to-event data modeling which is common in health data analysis. There is a large body of statistics literature on this topic and some recent ML approaches, such as DeepSurvival (Blei lab) and DeepHit (van der Shaar lab). The work proposes a multi-task model to consider competing risks, similar to DeepHit. The authors do compare with other methods, but I think these comparisons fail in two aspects: a) no comparison with a version of their method that considers each risk independently -- this comparison would be important to include in order to understand whether it is the flexibility from boosting trees or from the simultaneous modeling of multiple risks that leads to the improved performance. Apart from the limitations in the results part, I thought that the presentation of the methodology as a boosting method is not very clear.