the idea of CEM, and matching in general, is to prune observations from the data set.  That often changes the quantity of interest in return for reducing model dependence.  If you'd like to keep the same quantity of interest (pate or patt), then you can divide the sample into 2 parts -- the part CEM identifies as matched and which you can estimate with little model dependence, and the part which is much more model dependent.  you can produce estimates within each and then take the weighted average of the two to produce your ultimate estimate.  you'll find some examples of this in our papers at my web site.
best,
Gary

On Thursday, June 14, 2012, Fernando Terrés wrote:
Hello, CEM list:
I would like to use CEM with survey data. The sampling design is stratified with survey weights (1500 observations). I would like, if possible, to estimate population estimates (PATE and PATT).
My questions:
1. Is it possible to get this estimations with CEM? (if yes, how?)
2. Can imbalance be used with survey data? (if yes, how?)
3. I see that, with CEM, the researcher must specify interactions. Exist any kind of advice on which interactions to include with CEM?
After the matching, I would like to test mediator effects (SEM with Mplus), using the CEM matched observations and weights (perhaps the weights to be used are a product of CEM weights and survey weights?), and the correct sampling design (stratified, on the subpopultation of matched cases?).
Thanking you in advance,
Fernando


--
Gary
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Gary King - Albert J. Weatherhead III University Professor - Director, IQSS - Harvard University
GKing.Harvard.edu - King@Harvard.edu - @kinggary - 617-500-7570 - Asst 495-9271 - Fax 812-8581