Structural Equation Modeling (SEM) is a statistical methodology that combines factor analysis and multiple regression, allowing researchers to examine complex relationships among observed and latent variables. SEM is widely used in social sciences, psychology, and economics to test theoretical models and hypotheses about causal relationships. However, traditional SEM approaches often face limitations with large datasets, nonlinear relationships, and high-dimensional data.

Conversely, Machine Learning (ML) excels in handling large-scale datasets, capturing nonlinear patterns, and making accurate predictions. Techniques such as neural networks, decision trees, and ensemble methods have revolutionized data analysis across various fields. Despite their strengths, ML models are often viewed as "black boxes," lacking the interpretability that SEM provides.

The primary objective of this project is to develop a hybrid framework that merges the strengths of machine learning with structural equation modeling. This integration aims to enhance SEM's capabilities in handling complex, high-dimensional data while retaining its interpretability, thereby providing a more powerful tool for researchers.