This study aimed to utilize causal machine learning techniques, namely uplift modeling to develop a patient specific treatment recommendation system based on radiomic variables for prostate cancer patients. In this work, 1048 cases from ProstateNet, that underwent one of three types of treatment (radical prostatectomy, radiation therapy, and active surveillance), were utilized. In order to cover as many confounding variables as possible, the following were included: patient age, PSA blood levels, PI-RADS, index lesion location, gleason scores, ISUP grade and radiomic variables extracted from the whole gland or index lesion segmentations. Six different uplift algorithms were explored: S-learner, T-learner, X-learner, R-learner, uplift random forest and transformed outcome (TO) learner. The final model was a TO-learner where only whole-gland T2W radiomic features were included, reaching an area under the uplift curve (AUUC) of 0.5481 (out of a theoretical maximum of 0.7488). This model was used to split the patients in the hold-out test set into three groups, corresponding to each course of treatment. Additionally, nomograms were constructed to guide the treatment recommendation. It was found that younger patients, with low PI-RADS, low second gleason score, but higher ISUP and high PSA would benefit from RT, whereas the opposite characteristics would benefit from RP. Uplift modeling can be a valuable tool for identifying subgroups of patients who may benefit from specific treatments in healthcare.