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.

翻译：本研究重新审视了结构方程模型（SEM）与贝叶斯网络模型（BNM）在因果推断分析中的联合应用。研究视角聚焦于两种立场的争论：一方主张将BNM作为结构模型定义的探索阶段甚至唯一阶段，另一方则提倡将SEM作为探索性分析的更优选择。研究承认SEM与BNM各自具有的优势与局限，但重点评估了利用SEM强大的结构推断能力与BNM提供的动态概率建模之间的理论争议。本文以\citet{balaguer_2022}的研究为案例，展示了一个以积极教养（\textit{PP}）和学校氛围与功能感知（\textit{CFS}）为函数的个人积极青少年发展（\textit{PYD}）结构模型。论文最终明确主张SEM在探索性因果分析中的方法论优先地位，同时承认BNM在后续研究阶段中的潜在价值。