I am completing a study to analyze the effects of a policy treatment on a sub-population of adults (low-income adults, specifically) that are represented in a complex, nationally representative survey.
I want to conduct a matching procedure that balances members of this sub-population with respect to treatment (1=yes; 0=no). It is unclear to me, however, whether I should restrict my matching procedure to include only members of the sub-population of interest (i.e. low-income adults), or whether my matching procedures should include all individuals in the survey population and simply include income as a match variable.
I'm leaning towards the latter option (i.e. include the entire sample in matching) because I know that my matching weights need to be multiplied by the sampling weights to complete my subsequent outcome analysis, and existing software easily facilitates this transformation. However, it seems somewhat silly to include all members of the overall survey sample in my matching procedure, as the treatment of interest is only relevant to people with low-incomes. Therefore, my matching procedure would be including numerous irrelevant observations.
My question here is as follows: is it bad statistical practice to conduct my matching procedure on the overall survey population even though the treatment of interest is only relevant to a small fraction of that population?