Abstract
Background. Within substance abuse research, quantitative
methodologists tend to view randomized controlled trials (RCTs)
as the “gold standard” for estimating causal effects, in part due to
experimental manipulation and random assignment. Such methods are
not always possible due to ethical and other reasons. Causal directed
acyclic graphs (causal DAGs) are mathematical tools for (1) precisely
stating researchers’ causal assumptions and (2) providing guidance
regarding the specification of statistical models for causal inference
with nonexperimental data (such as epidemiological data). Purpose.
This manuscript describes causal DAGs and illustrates their use in
regards to a long standing theory within the field of substance use: the
gateway hypothesis. Design. Data from the 2013 National Survey of
Drug Use and Health are utilized to illustrate the application of causal
DAGs in model specification. Then using the model specification
constructed via causal DAGs, logistic regression models are used to
generate odds ratios of the likelihood of trying heroin, given that one
has tried alcohol, marijuana, and/or tobacco. Conclusion. Granting the
assumptions encoded in specific causal DAGs, researchers, even in the
absence of RCTs, can identify and estimate causal effects of interest
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