Integration of Structural Equation Modeling and Bayesian Networks in the Context of Causal Inference: A Case Study on Personal Positive Youth Development

TL;DR

This paper compares structural equation modeling and Bayesian networks for causal inference, advocating for SEM's primary role in exploratory analysis while recognizing Bayesian networks' potential in subsequent phases, demonstrated through a youth development case study.

Contribution

It provides a critical evaluation of SEM and BNM integration in causal inference, emphasizing SEM's primacy and illustrating their combined use in a youth development context.

Findings

SEM is recommended for exploratory causal analysis.

BNM has potential in subsequent confirmatory phases.

Case study demonstrates practical application of combined methods.

Abstract

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.