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Johns Hopkins Bloomberg School of Public Health Abstract: 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 work addresses these concerns by explicitly considering what covariates the outcome disparity and hypothetical intervention adjust for (so-called allowable covariates) and the equity value judgments these choices convey, drawing from the bioethics, biostatistics, epidemiology, and health services research literatures. F

Johnw jacksonMental healthJohns hopkins bloomberg school of public healthDepartment of epidemiology seminar seriesEpidemiology seminarHopkins bloomberg schoolமன ஆரோக்கியம்ஜான்ஸ் ஹாப்கின்ஸ் ப்ளூம்பெர்க் பள்ளி ஆஃப் பொது ஆரோக்கியம்துறை ஆஃப் தொற்றுநோய் கருத்தரங்கு தொடர்தொற்றுநோய் கருத்தரங்குஹாப்கின்ஸ் ப்ளூம்பெர்க் பள்ளி