Causal decomposition analyses can contribute to the evidence base for interventions that address health inequities. Through study design and assumptions, they  rule out alternate explanations such as confounding, selection-bias, and measurement error . However, their practical use is impeded by critical challenges. First, current approaches pay little attention to what variables a disparity measure itself should condition on or standardize over. Second, current approaches have ignored what hypothetical interventions should condition on and thus may not reflect equity concerns that actual interventionists would respect . Third, there are several estimators across the epidemiology and economics literature but their incorporation of equity value judgements has not been examined, and these do not cover many of the ways in which disparities and hypothetical interventions may be defined. In this paper , motivated by the clinical example of treatment intensification and hypertension control disparities, we address these issues of defining and estimating meaningful causal decompositions. We present a framework that explicitly considers what the disparity measure and hypothetical intervention account for and how these choices can be mapped to notions of equity . For these general estimands we provide identifying assumptions and estimators based on adaptations of ratio-of-mediator probability and inverse-odds-ratio weighting and evaluate their statistical performance .
Causal decomposition analyses can help build the evidence base for interventions that address health disparities (inequities). They ask how disparities in outcomes may change under hypothetical intervention. Through study design and assumptions, they can rule out alternate explanations such as confounding, selection-bias, and measurement error , thereby identifying potential targets for intervention. Unfortunately, the literature on causal decomposition analysis and related methods have largely ignored equity concerns that actual interventionists would respect , limiting their relevance and practical value. This paper addresses these concerns by explicitly considering what covariates the outcome disparity and hypothetical intervention adjust for (so-called allowable covariates) and the equity value judgements these choices convey, drawing from the bioethics, biostatistics, epidemiology, and health services research literatures. From this discussion, we generalize decomposition estimands and formulae to incorporate allowable covariate sets, to reflect equity choices, while still allowing for adjustment of non-allowable covariates needed to satisfy causal assumptions . For these general formulae, we provide weighting-based estimators based on adaptations of ratio-of-mediator-probability and inverse-odds-ratio weighting . We discuss when these estimators reduce to already used estimators under certain equity value judgements, and a novel adaptation under other judgements .
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disparities (inequities). They ask how disparities in outcomes may change under hypothetical intervention.
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. However, their practical use is impeded by critical challenges. First, current approaches pay little attention to what variables a disparity measure itself should condition on or standardize over. Second, current approaches have ignored what hypothetical interventions should condition on and thus may not reflect
, thereby identifying potential targets for intervention. Unfortunately, the literature on causal decomposition analysis and related methods have largely ignored
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. Third, there are several estimators across the epidemiology and economics literature but their incorporation of equity value judgements has not been examined, and these do not cover many of the ways in which disparities and hypothetical interventions may be defined. In this paper , motivated by the clinical example of treatment intensification and hypertension control disparities, we address these issues of defining and estimating meaningful causal decompositions. We present a framework that explicitly considers what the disparity measure
, limiting their relevance and practical value. This paper addresses these concerns by explicitly considering what covariates the outcome disparity
R
account for and how these choices can be mapped to notions of equity
adjust for (so-called allowable covariates) and the equity value judgements these choices convey, drawing from the bioethics, biostatistics, epidemiology, and health services research literatures. From this discussion, we generalize decomposition estimands and formulae to incorporate allowable covariate sets, to reflect equity choices, while still allowing for adjustment of non-allowable covariates needed to satisfy causal assumptions
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estimands we provide identifying assumptions and
formulae, we provide weighting-based
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ratio-of-mediator probability
ratio-of-mediator-probability
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and evaluate their statistical performance
. We discuss when these estimators reduce to already used estimators under certain equity value judgements, and a novel adaptation under other judgements