The neighborhood food environment has been shown to influence diet quality and obesity in observational research, yet several studies have reported weak or null associations. These inconsistencies may be due to a lack of complex models that account for potential threats to causal inference, such as bias resulting from individuals selectively locating in neighborhoods with “healthy” food outlets (or vice-versa), or the purposeful placement of food stores and restaurants in neighborhoods over time. Previous studies in the food environment and health literature have not explicitly accounted for unobserved heterogeneity, thus information regarding the magnitude and direction of these biases is lacking. To address these limitations, we used over 25 years of individual-level data from the Coronary Artery Risk Development in Young Adults (CARDIA) study, with temporally and geographically-linked food outlet locations and neighborhood sociodemographics. We explicitly sought to quantify longitudinal associations between the neighborhood food environment with diet quality and body mass index (BMI) using causal models to account for unobserved heterogeneity; and subsequently, to assess the magnitude and direction of bias by comparing results to estimates derived from less complex models. The results showed that residential location and the placement of food stores and restaurants were influenced by the food environment over time (i.e., reverse pathways from food environment outcomes to unmeasured factors); thus causal inference in the context of observational neighborhood research is not possible without complex modeling approaches. We also found that the magnitude of associations between the neighborhood food environment and diet quality was approximately twenty times higher using instrumental-variables regression compared to models that do not account for unobserved heterogeneity, with similar findings for BMI. These results suggest that previous studies have underestimated associations between the neighborhood food environment and health outcomes. Our research suggests that inconsistent findings in the existing literature may, in part, result from a lack of control for residential self-selection bias and the purposeful placement of food stores and restaurants. Therefore, it is critical that future observational studies account for unobserved heterogeneity with more complex models. Our findings can also be used to inform future intervention and policy changes to modify the neighborhood food environment.