But note that teffects reports a very different standard error (we'll discuss why that is shortly), plus a Z-statistic, p-value, and 95% confidence interval rather than just a T-statistic.
The average treatment effect on the treated is identical, other than being rounded at a different place. Teffects psmatch (y) (t x1 x2, probit), atet So to run the same model using teffects type: The teffects command uses a logit model by default, but will use probit if the probit option is applied to the treatment equation. Second, psmatch2 by default uses a probit model for the probability of treatment. The teffects command by default reports the average treatment effect (ATE) but will calculate the average treatment effect on the treated (which it refers to as ATET) if given the atet option. First, psmatch2 by default reports the average treatment effect on the treated (which it refers to as ATT). However, the default behavior of teffects is not the same as psmatch2 so we'll need to use some options to get the same results. Teffects psmatch (outcome) (treatmentcovariates) The basic syntax of the teffects command when used for propensity score matching is: You can carry out the same estimation with teffects. 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. Propensity Score Matching in Stata using teffectsįor 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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