Objective The aim of this paper is to describe the basic ideas and algorithms of random forest propensity scores method for controlling confounders and apply it in detecting drug adverse reaction signals.Methods First,we used random forest to calculate a patient’s probability of taking bisphosphonates.Then,we analyzed the association of bisphosphonate intake with risk of fracture by controlling potential confounders with propensity score method.The controlling confounders methods included 1:1 matching,1:M matching and regression adjustment by using the propensity score calculated by random forest.The results were compared with those from logistic propensity score.Results The results of random forest propensity score and logistic propensity score were comparable.One to one propensity score matching cause a lot of sample lost and its results were quite different from those based on other methods.Conclusion Random forest propensity score method can reduce the confounding bias.Hence,it could be used as an alternative to and verification of the logistic propensity score in controlling confounders.However,1:1 propensity score matching may not be suitable for adverse drug reaction data from a spontaneous reporting system.