![]() You can carry out the same estimation with teffects. does not take into account that the propensity score is estimated. Variable Sample | Treated Controls Difference S.E. The psmatch2 command will give you a much better estimate of the treatment effect: (Regressing y on t, x1, and x2 will give you a pretty good picture of the situation.) Thus simply comparing the mean value of y for the treated and untreated groups badly overestimates the effect of treatment: ![]() However, the probability of treatment is positively correlated with x1 and x2, and both x1 and x2 are positively correlated with y. This is constructed data, and the effect of the treatment is in fact a one unit increase in y. It consists of four variables: a treatment indicator t, covariates x1 and x2, and an outcome y. Run the following command in Stata to load an example data set: We thus strongly recommend switching from psmatch2 to teffects psmatch, and this article will help you make the transition. This often turns out to make a significant difference, and sometimes in surprising ways. 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. However, Stata 13 introduced a new teffects command for estimating treatments effects in a variety of ways, including propensity score matching. Note: readers interested in this article should also be aware of King and Nielson's 2019 paper Why Propensity Scores Should Not Be Used for Matching.įor many years, the standard tool for propensity score matching in Stata has been the psmatch2 command, written by Edwin Leuven and Barbara Sianesi.
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