Control groups can provide counterfactual evidence for assessing the impact of an event or policy change on a target variable. We argue that Ötting a time series model o§ers potential gains over a direct comparison between the target and a weighted average of controls. More importantly, it highlights the assumptions underlying methods such as di§erence-in-di§erences and synthetic control, suggesting ways to test these assumptions. Gains from simple and transparent time series models are analysed using examples from the literature, including the California smoking law of 1989 and German re-uniÖcation. We argue that selecting controls using a time series strategy is preferable to existing data-driven regression methods.(joint with Stephen Thiele)