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There have been many claims in the media and a bit of respectable researchabout the causes of variation in firearm sales. The challenges for causalinference can be quite daunting. This paper reports an analysis of dailyhandgun sales in California from 1996 through 2018 using an interrupted timeseries design and analysis. The design was introduced to social scientists in1963 by Campbell and Stanley, analysis methods were proposed by Box and Tiao in1975, and more recent treatments are easily found (Box et al., 2016). But thisapproach to causal inference can be badly overmatched by the data on handgunsales, especially when the causal effects are estimated. More important forthis paper are fundamental oversights in the standard statistical methodsemployed. Test multiplicity problems are introduced by adaptive model selectionbuilt into recommended practice. The challenges are computational andconceptual. Some progress is made on both problems that arguably improves onpast research, but the take-home message may be to reduce aspirations aboutwhat can be learned.

Post-Model-Selection Statistical Inference with Interrupted Time Series Designs: An Evaluation of an Assault Weapons Ban in California