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, April 6, 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: Richard Berk (University of Pennsylvania)
• Title: Firearm Sales in California Through the Myopic Vision of an Interrupted Time Series Causal Analysis
• Discussant: John Donohue (Stanford)
• Abstract: There have been many claims in the media and a bit respectable research about the causes of variation in firearm sales. The challenges for causal inference can be quite daunting. In this talk, I report on an analysis of daily firearm sales in California from 1996 through most of 2018 using an interrupted time series design and analysis. The design was introduced to social scientists in 1963 by Campbell and Stanley, analysis methods were proposed by Box and Tiao in 1975, and more recent treatments are easily found (Box et al., 2016). But this approach to causal inference can be badly overmatched by the data on firearm sales, especially when the causal effects of gun control measures are estimated. For example, there can be dramatic responses to a wide variety of abrupt “shocks” to the sales data that can introduce serious and unanticipated confounding (e.g., the mass shooting in Las Vegas in 2017). Perhaps more important for this online gathering are fundamental oversights in the standard statistical methods employed. Test multiplicity problems are introduced by adaptive model selection built into recommended practice. The challenges are computational and conceptual. Some progress is made on both problems that arguably improves on past research, but the take-home message may be to reduce aspirations about what can be learned.