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.
[MatchIt] Naive Estimator for ATT after Full Matching
2 messages · thebudget72 m@iii@g oii gm@ii@com, Bert Gunter
Do note, per the posting guide linked below (please read it if you haven't done so already): 1. *"Questions about statistics:* The R mailing lists are primarily intended for questions and discussion about the R software. However, questions about statistical methodology are sometimes posted. If the question is well-asked and of interest to someone on the list, it *may* elicit an informative up-to-date answer. " So do not be surprised if you do not get a response here. stats.stackexchange.com *may* be a better alternative if you do not. 2. "For questions about functions in standard packages distributed with R (see the FAQ Add-on packages in R <https://cran.r-project.org/doc/FAQ/R-FAQ.html#Add-on-packages-in-R>), ask questions on R-help. If the question relates to a *contributed package* , e.g., one downloaded from CRAN, try contacting the package maintainer first." The matchit package maintainer can be found by: maintainer("matchit") if you think the above applies. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Wed, May 5, 2021 at 11:55 AM <thebudget72 at gmail.com> wrote:
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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