[R] propensity score adjustment using R
Bunny, lautloscrew.com
bunny at lautloscrew.com
Thu Sep 18 17:31:11 CEST 2008
Hi all,
i am looking to built a simple example of a very basic propensity
score adjustment, just using the estimated propensity scores as
inverse probability weights (respectively 1-estimated weights for the
non-treated). As far as i understood, MLE predictions of a logit model
can directly be used as to estimates of the propensity score.
I already considered the twang package and the several matching
approaches and i am basically not trying to reinvent the wheel. Often
i could not understand what was going, and why some iterative process
like k.stat.max were taking so long.
Anyway i´d really like to something really simple apart from all this
focus on some iterative algorithm thats beyond my scope.
And here is where the problem starts. Most textbooks i considered
proposed to estimate a simple logit model by ML Estimation. Obviously
the standard approach to do it using R is glm. The zelig package
provides an alternative. My logit model is as simple at its gets:
Y~X, where Y is a treament vector and X is matrix of some covariates.
I wonder right now if te glm respectively summary(glm(...)) puts out
something comparable to ML estimates that can be used as the estimated
pscores, in such a way that there is one value for every observation.
Thanks for any help in advance
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