ABSTRACT
Objective In observational studies or non-randomized design,the researchers' ability to make causal inferences from data was hampered by confounding factors.This study used this method to analyze a group of observational medical data in order to instruct relevant medical personnel to carry out their own causal inference studies.Methods At present,the four main types of propensity scoring methods:matching,stratification,inverse probability weighting and covariate adjustment have been widely used in the study of causal inference.Propensity score method can theoretically eliminate the bias of the observable confounding factors,so that the treatments variables are close to the result of random assignment design,thus,it is estimated that the treatment factor has a causal effect on the outcome.Results Considering the advantages of the inverse probability weighting method over other methods,this paper summarizes the applicable conditions for the estimate of causal effect,particularly illustrates the use of a modern nonparametric statistical technology--Generalized Boosted Models (GBM) and its advantages and disadvantages.Conclusion When there is a lot of different types of confounding factors,and uncertain functional forms for their associations with treatment selection in linear,non-linear or interaction effect,and other issues,GBM propensity score weighting method can overcome the obstacles in the process of accurately estimating propensity score.