In some data set A, we have: household id, person id, age, sex, and then a simple binary
likes donuts / does not like donuts variable.
In some other data set B, we have just household id, person id, age, and sex.
We have set up a regression in data set A with independent variables: age, sex, and
does anyone in the household like donuts? in order to predict who likes donuts in data set B.
People living in households where anyone likes donuts are more likely to like donuts themselves, so we don't want to throw out that intra-household probability when imputing donut likeage.
However, I'm unclear how to implement something computationally that maintains these within-household donut affinities when we're imputing
do you like donuts in data set B?
Do we impute at the household-level first, and then impute a subset of the people within sampled households? I feel like I should take a page out of the cluster sampling playbook, but I'm not really sure where to start looking.