Online Causal Inference Seminar

Free and open to the public
All seminars are on Tuesdays at 8:30 am PT (11:30 am ET / 4:30 pm London / 5:30 pm Berlin).

Tuesday, March 30, 2021 [Link to join] (ID: 995 8569 5110, Password: 007080)

(New non-US times due to daylight-savings time: 8:30 am PT / 11:30 am ET / 3:30 pm London / 4:30 pm Berlin / 11:30 pm Beijing)

• Speaker: Elizabeth Stuart (Johns Hopkins University)
• Title: Using stacked comparative interrupted time series to estimate opioid policy effects
• Discussant: Laura Hatfield (Harvard)
• Abstract: 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, called “stacked comparative interrupted time series” (CITS), which defines cohorts of states that implemented a policy at the same time, finds comparison states at that point in time, compares outcomes between the intervention and comparison states, and then “stacks” a number of these individual CITS designs on top of each other to account for the staggered implementation. 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.