I've completed propensity score matching of a treatment and control group across a number of covariates. On two categorical covariates we require an exact match, for example, Gender and Eye Color, and we're trying to determine effect of a treatment on continuous feature like salary (silly example).
However, there is one group of, say females with brown eyes, that are under represented in the unmatched data. Because of the exact matching, this causes males with brown eyes to be under represented in the resulting matched data (regardless of their significant representation in the unmatched data). I'm trying to determine how this could be impacting our salary prediction and quantifying the potential error due to bias in the matched sample.
Put another way, what is the potential error in the matched data due to differences in covariate distribution between matched and unmatched samples?
I've gone down the path of assessing confounding variables and effect modification, but I'm not sure 1) if I can compare those effects across populations and 2) how I should present the information . I might be over complicating, but it would seem that I need to adjust for the confounding features and than adjust the effect modification by the distribution in the matched data? I've also tried estimating treatment effects after matching , bootstrapping, and rbounds  but none seem to be addressing what I need-- I suspect that I'm just phrasing the question wrong or missing the obvious?
 https://sphweb.bumc.bu.edu/otlt/MPH-Modules/PH717-QuantCore/PH717_MultipleVariableRegression/PH717_MultipleVariableRegression5.html  https://cran.r-project.org/web/packages/MatchIt/vignettes/estimating-effects.html#after-pair-matching-without-replacement  https://cran.r-project.org/web/packages/rbounds/rbounds.pdf  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2943670/