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Causal decomposition analyses can help build the evidence base forinterventions that address health disparities (inequities). They ask howdisparities in outcomes may change under hypothetical intervention. Throughstudy design and assumptions, they can rule out alternate explanations such asconfounding, selection-bias, and measurement error, thereby identifyingpotential targets for intervention. Unfortunately, the literature on causaldecomposition analysis and related methods have largely ignored equity concernsthat actual interventionists would respect, limiting their relevance andpractical value. This paper addresses these concerns by explicitly consideringwhat covariates the outcome disparity and hypothetical intervention adjust for(so-called allowable covariates) and the equity value judgements these choicesconvey, drawing from the bioethics, biostatistics, epidemiology, and healthservices research literatures. From this discussion, we generalizedecomposition estimands and formulae to incorporate allowable covariate sets,to reflect equity choices, while still allowing for adjustment of non-allowablecovariates needed to satisfy causal assumptions. For these general formulae, weprovide weighting-based estimators based on adaptations ofratio-of-mediator-probability and inverse-odds-ratio weighting. We discuss whenthese estimators reduce to already used estimators under certain equity valuejudgements, and a novel adaptation under other judgements.

Meaningful causal decompositions in health equity research: definition, identification, and estimation through a weighting framework