Covariate adjusted regression (CAR) has become a gold standard for analyzing multivariable models. This approach falls short in many clinical settings, specifically, those with observational data. Due to the popularity of CAR, but its limitations, RTI International and the Chronic Effects of Neurotrauma Consortium (CENC) explored a 3-method approach for analyzing causal relationships with observational clinical data. Using combination of standard CAR, inverse propensity score weighted regression (IPSW), and structural equation modeling (SEM), we assessed the causal relationship between mild traumatic brain injury (mTBI) exposure and various outcome measures. The combination of all three approaches, CAR, IPSW, and SEM, provided a robust causal analysis. By fitting models with all three methods, we maximized the benefits of each method, cross checked assumptions, and built a comprehensive final model. Based on performance in this study, this three-method approach will translate best to observational studies interested in "kitchen sink" models where there are many factors under consideration and large causal models where the role of each factor on the causal pathways is unclear.