I want to assess the effect of a treatment. I have matched 2 groups (through CEM) on 3 covariates (A, B and C) at Time 1. Now, i want to perform linear mixed modelling on the matched dataset (with a random intercept per subject & school), while including different covariates that were not used for the matching procedure (covariate C & D). I am doing it this way so I can assess the effect of treatment over time.

So far, this is my code for such a regression:

fit <- lmer(Outcome ~ Group + A + B + C + D + E + (1|Subject) + (1|School), data = md, weights = weights

Is this the correct approach to include the matching weights in a regression?