When conceptualizing and designing a study, or when developing plans
to test a research question, it is important to draw a directed acyclic
graph (DAG). DAGs, like path
diagrams, are causal diagrams. Causal diagrams depict the
hypoothesized causal processes that link two or more variables. Path
diagrams are typically used after analysis to describe and report the
findings in analysis (when using path analysis, factor analysis, or structural equation modeling). By contrast, DAGs are
particularly useful when designing a study or before analysis, because
they can help specify which variables it is important to control for
and—just as importantly—which variables it is important not to control
for.
When drawing a DAG for your study, draw all the variables that link
the hypothesized cause to the hypothesized effect, including
confounders, mediators, and colliders. In your study, it is important to
control for confounders. Moreover, it is important not to control to
control for mediators when you are interested in the total effect of the
predictor on the outcome. In addition, it is important not to control
for descendants of the outcome variable. When there is a collision, it
is important not to control for the collider when examining the
association between the two causes of the collider. The only time when
one should control for a collider is when the collider is also a cause
(i.e., confound) of both the predictor and outcome variable rather than
a common effect of both.
