How is propensity score matching used in Stata 13?
However, Stata 13 introduced a new teffects command for estimating treatments effects in a variety of ways, including propensity score matching. The teffects psmatch command has one very important advantage over psmatch2: it takes into account the fact that propensity scores are estimated rather than known when calculating standard errors.
How to switch from psmatch2 to teffects in Stata?
We thus strongly recommend switching from psmatch2 to teffects psmatch, and this article will help you make the transition. Run the following command in Stata to load an example data set: It consists of four variables: a treatment indicator t, covariates x1 and x2, and an outcome y.
How are teffects used in propensity score matching?
Thus you supply the stem of the variable name, and teffects psmatch will add suffixes as needed. In this case each observation is only matched with one other, so gen (match) only creates match1. Referring to the example output, the match of observation 1 is observation 467 (which is why those two are listed).
When to use teffects psmatch to estimate ate?
By default, teffects psmatch estimates the ATE by matching each subject to a single subject with the opposite treatment whose propensity score is closest. Sometimes, however, we may want to ensure that matching occurs only when the propensity scores of a subject and a match differ by less than a specified amount.
Which is better psmatch or teffects for propensity scores?
The teffects psmatch command has one very important advantage over psmatch2: it takes into account the fact that propensity scores are estimated rather than known when calculating standard errors. This often turns out to make a significant difference, and sometimes in surprising ways.
How is propensity score matching used in regression?
Regression on the “Matched Sample”. Another way to conceptualize propensity score matching is to think of it as choosing a sample from the control group that “matches” the treatment group. Any differences between the treatment and matched control groups are then assumed to be a result of the treatment.