Recommendation 1: Strengthen and promote analytic methods that maximize the ability to draw causal inferences from observational studies and enable a better understanding of health disparity causes.

Recommendation 2: Incorporate and further develop models that reflect the multilevel nature of health disparity causes to provide richer and more accurate characterizations of plausible causal pathways.

Recommendation 3: Expand the use of complex systems and simulation modeling to increase the ability to model intricate relationships between health disparities and health determinants and to assess health disparities interventions.

Recommendation 4: Incorporate the further use of qualitative and mixed methods analysis so participant perspectives can illuminate plausible causal mechanisms and provide better understanding of the impacts of policies and interventions.