Error-in-variables regression is a common ingredient in treatment effect estimators using panel data. This includes synthetic control estimators, counterfactual time series forecasting estimators, and combinations. We study high-dimensional least squares with correlated error-in-variables with a focus on these uses. We use our results to derive conditions under which the synthetic control estimator is asymptotically unbiased and normal with estimable variance, permitting inference without assuming time-stationarity, unit-exchangeability, or the absence of weak factors. These results hold in an asymptotic regime in which the number of pre-treatment periods goes to infinity and the number of control units can be much larger $(p \gg n)$.