Propensity score matching using -psmatch-

In observational studies, the researcher has no control over treatment assignment. Control and intervention groups are therefore often unbalanced with respect to confounding variables, and even covariate adjustment doesn't always fully eliminate bias. The propensity score is the conditional probability of being in the treatment group given the covariates, and it can be used to balance the covariates in the two groups. The score is derived from a logistic regression model of treatment group on the covariates, with the propensity score being the predicted probability of being in the treated group. Once calculated, the propensity score can be used to reduce bias by matching, stratification, or by using it as a covariate in the regression model. In this presentation, I will briefly present some of the theory behind the use of propensity scores, and demonstrate the Stata procedure psmatch, which facilitates propensity score matching.