Hi,



I was wondering if I can have some advice on the following problem.



Let's say that I have a problem in which I want to predict a binary outcome
and I use logistic regression for that purpose. In addition, suppose that my
model includes predictors that will not be used in scoring new observations
but must be used during model training to absorb certain effects that could
bias the parameter estimates of the other variables.



Because one needs to have the same predictors in model development and
scoring, how it is usually done in practice to overcome this problem? I
could exclude the variables that will not be available during scoring, but
that will bias the estimates for the other variables.


Many thanks for your help.

Lars.

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