In general (of course there will be exceptions), do you recommend imputing each missing value to produce a single data set with imputations rather or stripping out missing cases?   


On Wed, Sep 2, 2009 at 11:37 AM, Gary King <king@harvard.edu> wrote:


On 09/02/2009 12:20 AM, Donald Braman wrote:
I can't seem to subscribe to the MatchIt list, so I'm posting here in hopes that the same people read both.
I'm working with multiply imputed data from Amelia & wondering if any progress has been made at integrating Amelia, MatchIt, and Zelig -- in particular, pre-processing multiply imputed data and preparing them for use in Zelig.
Not at all pressing, and (again) apologies for posting here rather than the MatchIt list.
Cheers, Don

At the moment, MatchIt doesn't allow missing data on input (unless you temporarily code it as observed and exact match on the missing values).  In part, this is because most matching algorithms don't have procedures to deal with missingness.  An exception is CEM which has a procedure for multiply imputed data, but to access that you'll need to use the (R or Stata) version of CEM directly (see http://gking.harvard.edu/cem) instead of the one in MatchIt.

(separate message coming to fix your matchit subscription...)

Gary
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Gary King
Albert J. Weatherhead III University Professor
Director, Institute for Quantitative Social Science
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