From Path Coefficients to Targeted Estimands: A Comparison of Structural Equation Models (SEM) and Targeted Maximum Likelihood Estimation (TMLE)

Structural Equation Modeling (SEM) has gained popularity in the social sciences and causal inference due to its flexibility in modeling complex relationships between variables and its availability in modern statistical software. To move beyond the parametric assumptions of SEM, this paper reviews targeted maximum likelihood estimation (TMLE), a doubly robust, machine learning-based approach that builds on nonparametric SEM. We demonstrate that both TMLE and SEM can be used to estimate standard causal effects and show that TMLE is robust to model misspecification. We conducted simulation studies under both correct and misspecified model conditions, implementing SEM and TMLE to estimate these causal effects. The simulations confirm that TMLE consistently outperforms SEM under misspecification in terms of bias, mean squared error, and the validity of confidence intervals. We applied both approaches to a real-world dataset to analyze the mediation effects of poverty on access to high school, revealing that the direct effect is no longer significant under TMLE, whereas SEM indicates significance. We conclude with practical guidance on using SEM and TMLE in light of recent developments in targeted learning for causal inference.