Censored Quantile Regression Forests

In many applications, we want to predict and estimate the effect of a covariate on survival timeof interests. Examples include treatment, surgical procedure, or immunization on survival time of patients, who for example, could be individuals who have metastatic breast cancer, military casualties suffering from various injuries, or survival time of infectious diseases.Classically, most datasets have been too small to meaningfully examine the heterogeneity of the data beyond dividing them into a few subpopulations. In the past few years, however, there has been an explosion of experimental settings where it is potentially feasible to explore heterogeneity to its full extent. An impediment to exploring heterogeneous effects is the fear that scientists with two opposite agendas could hypothetically string together two opposite but coherent results by searching through many different possible models and then reporting only the very extreme ones - highlighting solely spurious results (Olken, 2015). Thus, protocols for clinical trials must specify in advance the pre-analysis plans and then learn from the data.