Abstract
In the statistical analysis of observational data, propensity score is a technique that attempts to estimate the effect of a treatment (exposure) by accounting for the covariates that predict receiving the treatment (exposure). The aim of this paper is to provide a brief guide for clinicians and researchers who are applying propensity score analysis as a tool for analyzing observational data. We reviewed literature about how, when and why propensity score is used and then we discussed some important practical issues in using propensity score in observational studies. Appling propensity score as a method for analyzing observational studies is very useful but, we should know when and how we can use this method. Moreover, new methods of propensity score analysis such as Bayesian and doubly robust approaches were established in recent years, and these methods could be more useful for researchers in estimating causal effect from observational studies.