  Propensity score plays a central role in causal inference, but its use is not
limited to causal comparisons. As a covariate balancing tool, propensity score
can be used for controlled descriptive comparisons between groups whose
memberships are not manipulable. A prominent example is racial disparities in
health care. However, conceptual confusion and hesitation persists for using
propensity score in racial disparities studies. In this commentary, we argue
that propensity score, possibly combined with other methods, is an effective
tool for racial disparities analysis. We describe relevant estimands, target
population, and assumptions. In particular, we clarify that a controlled
descriptive comparisons require weaker assumptions than a causal comparison. We
discuss three common propensity score weighting strategies: overlap weighting,
inverse probability weighting and average treatment effect for treated
weighting. We further describe how to combine weighting with the
rank-and-replace adjustment method to produce racial disparity estimates
concordant to the Institute of Medicine's definition. The method is illustrated
by a re-analysis of the Medical Expenditure Panel Survey data.
