Before revision:  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 .
After revision:  Through study design and assumptions, they 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 .