Many firearm, COVID-19, and opioid policies are implemented at the state level; as one example, 37 states have passed laws limiting the dose and/or duration of opioid prescriptions. However, studying state policy effects can be challenging, especially when states that do and don’t implement the policies differ from one another, and when states implement laws across time (staggered implementation); recent work has shown that standard “two way fixed effects” analysis approaches can lead to substantial bias, and the methodological literature providing solutions to this problem is growing rapidly. In this work we highlight a design-based approach to policy evaluation, in particular using augmented synthetic controls to estimate the effects of state policies regarding opioid prescribing. The talk will discuss the design and analysis choices made, the use of individual-level health insurance claims data in this context, and the interpretation of study results for broad audiences. The lessons have relevance to a broad range of policy questions.