Augmented synthetic controls in practice:  Studying the effects of state opioid prescribing laws

Many opioid policies are being 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 take a design-based approach, using augmented synthetic control methods,  to estimate state-specific policy effects.  Benefits of the approach include careful attention to design, the ability to examine balance during the pre-period, and a design that ensures conditioning only on pre-treatment measures. This talk will give an overview of some of the recent methodological innovations, and also discuss the practical challenges that arise when using these methods in practice. The work is particularly motivated by studies using large-scale medical insurance claims data to data to estimate the effects of opioid policies on prescribing patterns and overdoses, which raise questions around topics including variability in policy implementation and how to take advantage of the individual-level data available.