A Review of the Causal Decomposition Framework for Modeling Interventions that Reduce Disparities

This review summarizes recent developments in causal decomposition analysis (CDA), a modeling framework for reducing disparities. Rather than describing the current or past drivers of a disparity, CDA estimates the effect of an intervention to change the distribution of a variable or set of variables that are distributed differently or have different effects between groups. Furthermore, CDA clarifies how, through covariate adjustment, ethics and justice are implicit in any definition of disparity and may be incorporated into an intervention.

CDA has been applied to disparities in health, sociology, education, and computer science. The CDA framework consists of four steps: formulating a meaningful estimand, articulating identification assumptions to link an appropriate dataset with the estimand, choosing an appropriate estimator, and conducting statistical inference. Estimators have been developed for various types of data and to address particular statistical challenges. However, some estimators adjust for all available covariates in all parts of the model, without discussing ethical implications. Meanwhile, the literature has covered some but not all potential violations of standard CDA modeling assumptions.

CDA builds on previous methods for studying disparities by articulating causal estimands that transparently reflect implicit value judgements about health disparities. This review outlines the broad framework of CDA methodology, selected implementations, practical considerations, and current limitations and alternatives.