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Activity Number:
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247
- Causal Inference and Statistical Learning of Intervention and Policy Effects
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Health Policy Statistics Section
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Abstract #318672
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Title:
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Statistical Methods for Testing the Impact of State-Level Marijuana Laws on Substance Use Using Published Prevalence Estimates
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Author(s):
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Christine Mauro* and Amy Pitts
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Companies:
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Columbia University and Columbia University
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Keywords:
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difference-in-difference;
policy evaluation;
causal inference
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Abstract:
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Colorado and Washington legalized the use of recreational marijuana in 2012; since then, 15 more states and DC have passed laws. Evaluating the impact of these laws on substance use and substance use disorders is of critical public health importance. Available data are state-level two-year aggregated prevalence and standard error estimates of past-month substance use from the National Survey on Drug Use and Health. This talk will present two ongoing projects related to this research question. We first explore extending difference-in-difference methods to assess the impact of these laws on a state-by-state basis using simulated datasets constructed from the published prevalence estimates to account for the standard error of these estimates. Next, we consider estimating the average effect of law passage from all states with recreational laws using two different approaches: 1) a traditional difference-in-difference design with state treated as a fixed effect and 2) a linear mixed-effects model with a state-level random effect and fixed effect for law passage. The two modelling approaches are evaluated using simulation studies built on the real data assuming a range of effect sizes.
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Authors who are presenting talks have a * after their name.
