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Causal decomposition analysis provides a way to identify mediators thatcontribute to health disparities between marginalized and non-marginalizedgroups. In particular, the degree to which a disparity would be reduced orremain after intervening on a mediator is of interest. Yet, estimatingdisparity reduction and remaining might be challenging for many researchers,possibly because there is a lack of understanding of how each estimation methoddiffers from other methods. In addition, there is no appropriate estimationmethod available for a certain setting (i.e., a regression-based approach witha categorical mediator). Therefore, we review the merits and limitations of theexisting three estimation methods (i.e., regression, weighting, and imputation)and provide two new extensions that are useful in practical settings. Aflexible new method uses an extended imputation approach to address acategorical and continuous mediator or outcome while incorporating anynonlinear relationships. A new regression method provides a simple estimatorthat performs well in terms of bias and variance but at the cost of assuminglinearity, except for exposure and mediator interactions. Recommendations aregiven for choosing methods based on a review of different methods andsimulation studies. We demonstrate the practice of choosing an optimal methodby identifying mediators that reduce race and gender disparity incardiovascular health, using data from the Midlife Development in the US study.

Choosing an Optimal Method for Causal Decomposition Analysis: A Better Practice for Identifying Contributing Factors to Health Disparities