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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