Causal decomposition framework for quantifying intervenable cancer health disparities.

This study proposes a nonparametric decomposition framework to quantify and prioritize the impact of multiple intervenable mediators contributing to disparities in health outcomes. Identifying modifiable components of health disparities is essential for informing policy and guiding resource allocation, but several methodological challenges remain. These include formulating hypothetical interventions for immutable characteristics such as race or ethnicity, mitigating bias from potentially incompatible statistical models used to represent causal pathways, and evaluating multiple mediators for their joint and individual effects. To address these challenges, we integrate recent advances in health disparities research with modern machine learning techniques. A key assumption, common to causal disparity frameworks, is the absence of unmeasured confounding in the mediator-outcome relationship. We extend existing methods in mediation analysis and introduce a nonparametric sensitivity analysis that provides sharp bounds on modifiable disparity estimates in the presence of unmeasured confounding. Together, these methods offer a robust and flexible approach to causal disparity analysis, enabling the identification of high-impact, actionable interventions. We demonstrate the utility of the methods in the context of colorectal cancer survival disparities, a setting where persistent differences across racial, ethnic, and underserved populations in the United States underscore the urgent need for analytic strategies that uncover effective intervention points.