Propensity-score matching (PSM) is a statistical technique used to estimate the effect of a treatment by accounting for the covariates that predict receiving the treatment. It is particularly useful in observational studies where random assignment is not possible.

Key Steps in Propensity-Score Matching

1. Estimate Propensity Scores: Calculate the probability of each unit (e.g., individual) receiving the treatment based on observed covariates.
2. Match Units: Pair treated and control units with similar propensity scores.
3. Assess Balance: Check if the matched samples are balanced on covariates.
4. Estimate Treatment Effect: Compare outcomes between matched treated and control units to estimate the treatment effect.

Example in Stata

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 scores