Structural Equation

Structural equation modeling (SEM) is a statistical technique used to test hypotheses about causal relationships between multiple variables, often involving latent (unobserved) constructs. Current research focuses on extending SEM's capabilities to handle nonlinear relationships, high-dimensional data, and complex causal structures, employing methods like neural networks, boosting algorithms, and dynamic SEM to improve model accuracy and interpretability. These advancements are crucial for addressing challenges in diverse fields, including AI evaluation, environmental health risk assessment, and the understanding of human-AI trust dynamics, ultimately leading to more robust and reliable causal inferences.