The author is using the technical definitions of confounders and covariates without sufficient explanation, and the technical definitions do not match the normal English definitions.
In English, a confounder is any factor that distorts an observation. (My dictionary defines it as throwing into confusion or disarray.)
In causal inference, a confounder is a factor that is correlated with both treatment and outcome. If the treatment is randomly assigned, by construction it is independent of all other factors. This, there can be no confounders.
Your example is about observed occurrences of imbalance, but the technical definition is about probabilities. Observed imbalances can still skew inference, but that causes high variance (or low precision). It doesn't cause bias (or affect accuracy).
Adjusting for observed imbalances can reduce variance, but in some circumstances can actually cause bias.
In English, a confounder is any factor that distorts an observation. (My dictionary defines it as throwing into confusion or disarray.)
In causal inference, a confounder is a factor that is correlated with both treatment and outcome. If the treatment is randomly assigned, by construction it is independent of all other factors. This, there can be no confounders.
Your example is about observed occurrences of imbalance, but the technical definition is about probabilities. Observed imbalances can still skew inference, but that causes high variance (or low precision). It doesn't cause bias (or affect accuracy).
Adjusting for observed imbalances can reduce variance, but in some circumstances can actually cause bias.