Granger causality testing in mixed-frequency VARs with possibly (co)integrated time series is analyzed. The approach used works for stationary, integrated, or cointegrated variables, making it practical. The presence of non-stationary and trivially cointegrated high-frequency regressors leads to standard distributions when testing for causality on a parameter subset. Monte Carlo simulations and applications involving oil prices, consumer prices, GDP, and industrial production in Germany support this approach.