Dear R-help ML,
I would like to compute a Naive Estimator for the Average Treatment
Effect (ATT) after a Propensity Score Matching with full matching.
Since it is full matching, the resulting post-matching database contains
all the observations of the original dataset.
I came up with this code, which does a weighted average of the outcomes,
using the weights provided by the matching process, but I'm not sure
this is the correct way to achieve it.
How can I compute the ATT using a Naive Estimator after PSM?
I know I am supposed to do a regression, but I am interested in
computing a Naive Estimator as a difference between the means across the
two groups.
```r
library("MatchIt")
data("lalonde")
m.out2 <- matchit(treat ~ age + educ + race + married +
nodegree + re74 + re75,
data = lalonde,
method = "full",
distance = "glm",
link = "probit")
m.data2 <- match.data(m.out2)
te <- weighted.mean(m.data2$re78[m.data2$treat],
m.data2$weights[m.data2$treat])
nte <- weighted.mean(m.data2$re78[!m.data2$treat],
m.data2$weights[!m.data2$treat])
ne2w <- round(te-nte, 2)
print(paste0("The ATT estimated with a NE is: ", ne2w))
```
Thanks in advance and best regards.
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