  In this study, the combined use of structural equation modeling (SEM) and
Bayesian network modeling (BNM) in causal inference analysis is revisited. The
perspective highlights the debate between proponents of using BNM as either an
exploratory phase or even as the sole phase in the definition of structural
models, and those advocating for SEM as the superior alternative for
exploratory analysis. The individual strengths and limitations of SEM and BNM
are recognized, but this exploration evaluates the contention between utilizing
SEM's robust structural inference capabilities and the dynamic probabilistic
modeling offered by BNM. A case study of the work of, \citet{balaguer_2022} in
a structural model for personal positive youth development (\textit{PYD}) as a
function of positive parenting (\textit{PP}) and perception of the climate and
functioning of the school (\textit{CFS}) is presented. The paper at last
presents a clear stance on the analytical primacy of SEM in exploratory causal
analysis, while acknowledging the potential of BNM in subsequent phases.
