Model-Based and Design-Based Approaches to Longitudinal Causal Decomposition Analysis

This project will develop model-based and design-based approaches to identify risk factors that contribute to social disparities. Despite progress in various fields, large disparities in cognitive, economic, and health outcomes persist across social groups in the US based on various characteristics such as race/ethnicity. Since it is not possible to modify ascribed characteristics, the interests of disparity researchers and policymakers often center on identifying malleable risk factors that may play a role in reducing such disparities. However, there are limitations with the current methods for identifying risk factors. This project will address those limitations by developing a comprehensive framework that leverages longitudinal observational data and sequential randomized experimental designs to identify risk factors. Methodological guidance on how to use these approaches will be provided to researchers. An R package, sample code, and video tutorials will be developed. Graduate students will be mentored, and an undergraduate course on quantitative causal reasoning for the social sciences will be created at a Minority Serving Institution. This project will develop model-based and design-based approaches to causal decomposition that allow for time-varying risk factors (referred to as 'mediators') and outcomes. Current causal decomposition models are restricted. They only consider time-fixed mediators and outcomes and do not provide insight into the optimal time for interventions to reduce disparities. There also is the possibility of omitted variable bias, even with thorough sensitivity analysis, since the results are based on the researcher's subjective judgment on what constitutes a reasonable level of confounding. To develop a framework for analyzing longitudinal observational data, the project will use a generalized linear mixed model, incorporating age as a clock. This approach will enable the determination of when the effects of mediators' peak, fade, or remain flat. The results from this analysis could inform the timing of interventions, including whether sequential or simultaneous interventions may be more effective for reducing disparities over time. The project also will develop randomized sequential experimental designs that identify their effects (i.e., disparity reduction and remaining disparity). Experimental designs rely on fewer assumptions and allow researchers to test actual (rather than hypothetical) interventions to reduce disparities. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.