1. A method for training at least one artificial learning-capable system comprising the steps of: providing a predetermined training data set comprising a predetermined input data set and a predetermined outcome data set corresponding to input data for each of a respective predetermined number of subjects, observing survival data relating to patient survival of J subjects, recording covariates denoted x g (j) at a reference time t=0 relating to events that have not occurred for each subject in any order, recording special covariates denoted z p (j) relating to treatments received by each subject, assuming each subject represents a random sample drawn from a large pool of subjects with identical covariates x, z, defining the conditional probability S(t|x,z) for surviving to time t given x, z, estimating the p-th propensity score φ p corresponding to the probability for subject j to have treatment z p =1, categorizing the propensity scores into a number N p of categories, designated as strata, and augmenting the input data set and/or the outcome data set by the propensity scores and/or the stratum categorization, and training each artificial learning-capable system using the augmented input data set and/or the augmented outcome data set that was augmented according to the augmenting step, through the use of a computing device.