  Explanatory studies, such as randomized controlled trials, are targeted to
extract the true causal effect of interventions on outcomes and are by design
adjusted for covariates through randomization. On the contrary, observational
studies are a representation of events that occurred without intervention. Both
can be illustrated using the Structural Causal Model (SCM), and do-calculus can
be employed to estimate the causal effects. Pragmatic clinical trials (PCT)
fall between these two ends of the trial design spectra and are thus hard to
define. Due to its pragmatic nature, no standardized representation of PCT
through SCM has been yet established. In this paper, we approach this problem
by proposing a generalized representation of PCT under the rubric of structural
causal models (SCM). We discuss different analysis techniques commonly employed
in PCT using the proposed graphical model, such as intention-to-treat,
as-treated, and per-protocol analysis. To show the application of our proposed
approach, we leverage an experimental dataset from a pragmatic clinical trial.
Our proposition of SCM through PCT creates a pathway to leveraging do-calculus
and related mathematical operations on clinical datasets.
