there's 2 perspectives on this: one yes, and one no. Over time, some of
the same people have taken each of the two positions!
If we think of the data as drawn from a given joint distribution, then its
best to impute all the variables together. If instead, we're focused on a
causal inference then some think that its best to not impute the outcome
variable with the others. The first is more efficient; the latter is not
biased, but its conceivable that a researcher could fool themselves by
tweaking the imputation model to (perhaps inadvertently) favor themselves.
Gary
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*Gary King* - Albert J. Weatherhead III University Professor - Director,
IQSS - Harvard University
GKing.Harvard.edu <http://gking.harvard.edu/> - King(a)Harvard.edu -
@kinggary<http://twitter.com/kinggary>-
617-500-7570 - Asst 495-9271 - Fax 812-8581
On Thu, Feb 9, 2012 at 8:41 AM, Prashant <presearchwork(a)gmail.com> wrote:
Hi,
I have a hopefully quick question: Are there any prohibitions/issues
with imputing missing values for a treatment variable in a quasi-exp
or exp design (for example somehow treatment status wasn't reported in
an observational study or failed to be recorded in an experimental
study)? What about the dependent variable?
Thank you very much!
Prashant
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