High correlation between predictor and outcome I am fitting a linear model which is trying to predict a certain quantitative variable (volume after treatment). I am trying to make inference in which other variables influences this volume. One of this variables is the the volume before the treatment. I was wondering whether this volume before the treatment should be included in the model, as it is included in the post treatment volume and highly correlated

Yes, prior measurement should be included because it explains variance in your outcome and therefore, it will increase the sensitivity of your test. Also, it will make you more sure that you measured the treatment effect, and not just which group had higher/lower average volume already before treatment.
Another possibility is to test volume change instead of volume after treatment, but as far as I know, using the prior measurement as a covariate is the preferred option because it's more sensitive.
