I'm working on running mi impute on a data set with an unfortunate structure, where I have:

1) Several variables a, b, c that I am imputing but which have lots of logical skips and so are missing for nearly all of the data
2) The unfortunate circumstance that pretty much every observation is missing some variable, and in particular variable d tends to be missing when everything else isn't.

Now, this is fine when I'm trying to impute these variables (I can just use "if" with a, b, c so it doesn't try to impute their logical skips).

But what this means is that when I try to run mi impute, I get a lot of errors related to having no observations, because it tries to use a, b, c, d as predictors for the other variables, but then no observation has all the variables, and the model can't run.

I know the best-practice in MI is to use everything as a predictor but I don't think that works here. Is there a way around this? Perhaps some way to designate one of the imputed variables as not to be used as a predictor, or some way to impute a, b, c, d separately after everything else has already been imputed?

I'm not sure how important syntax is here but here's a simplified version of what I'm doing:

Code:
mi set wide

mi register imputed a b c d e

mi register regular f g h
    
    *SP17
mi impute chained (pmm if cond==1, knn(3)) a b c ///
    (ologit) d ///
    (logit) e  ///
    = f g h, add(15)