Kosuke Imai <kimai@...> writes:
Unfortunately, I don't think we have an automated procedure for
everything.
You would have to multiply
impute the data, do matching on each imputed data set,
and then combine it
in zelig() using mi() function.
But this does not require any programming. You can
simply run the same
matching procedure on each data set
via matchit() and then feed the resulting multiple
matched data sets into
zelig().
Good luck,
Kosuke
Department of Politics
Princeton University
http://imai.princeton.edu
On Sep 13, 2011, at 6:02 PM, Pingaul jb wrote:
> Dear Professor,
> I’m a post-doctoral student at Montreal University. I’m actually in
Columbia,
working and
propensity scores with a colleague and using MatchIt
and Zelig. First,
congratulations for your
packages that are very flexible.
>
> My question is about multiple imputation and propensity scores with
these
softwares. From what I
understand, combining both approaches would include:
>
> 1/ Doing multiple imputation and testing which variables to include.
>
> 2/ Propensity score analysis on each imputed data set and pooling the
overall
balance to check if it is ok
(or on each data set?).
>
> 3/ Calculation of the quantities of interest for each data set
>
> 4/ Pooling the quantities across data sets.
>
> I would like to know if there is a written syntax to perform the MatchIt
analysis for all of the imputed data
set without having to do it manually and check the
overall balance. Also,
in theory, the number of
individuals retained after propensity score matching
and the weights can
be different for each imputed
data set. So that we have to perform the final
analysis on each one and
then pool the data with a specific
procedure to take into account the eventual varying
Ns? I normally use
Mice package for multiple
imputation but it seems that Zelig handle Amelia. My
colleague seems to do
be able to do all that in stata,
but I’m not sure how to make all the three R packages
work together.
>
> I would be very happy if you could indicate to me a reference or a place
where I can find the syntax to do that
(I’ve been using R for some times so I can use
packages easily but I have
no programming skills).
Best Regards!
Jean-Baptiste
Hello:
I came across this posting from 2011. I am facing a similar analysis
problem with matching on multiply imputed data. I see the recommendation is
to run the matching on each imputed dataset separately, then to use zelig to
combine. How does this handle non-deterministic matching -- i.e., since the
matches for different datasets could be different, how can you combine
across imputations? Is it equally plausible to combine propensity scores or
raw values, then match on a single dataset? What about missing on the
matching variable itself?
Thanks,
Andrea