Propensity-score matching (PSM) in Stata is a powerful technique for estimating treatment effects from observational data. It helps to control for confounding variables by matching treated and control subjects with similar propensity scores, which are the probabilities of receiving the treatment given their covariates.
To perform PSM in Stata, you can use the teffects psmatch command. Here’s a basic example:
teffects psmatch (outcome) (treatment covariates)
For instance, if you have an outcome variable y, a treatment variable t, and covariates x1 and x2, the command would look like this:
teffects psmatch (y) (t x1 x2)
This command estimates the average treatment effect (ATE) by matching each treated subject with one or more control subjects based on their propensity scores12.
