In a study entitled Do Medical Marijuana Laws Increase Marijuana Use? Replication Study and Extension? published last week in the Annals of Epidemiology Sam Harper, Erin Strumpf and Jay Kaufman used some fun causal modelling techniques to estimate the impact of medical marijuana laws on marijuana use among teens.

They replicate findings from past studies using t-tests and random effects models (aka, multi-level models) then go on to show that difference in differences estimation can do a better job of estimating the effect of medical marijuana laws. Clearly this paper was controversial. Besides the controversy it shows that one of the most important components of research is a series of replications and improvements to past studies. If you are interested in causal modelling approaches (like difference in differences) this is a good applied introductory paper. Happy reading.

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