Disparities across race, gender, and class are important targets of descriptive research. But rather than only describe disparities, research would ideally inform interventions to close those gaps. The gap-closing estimand quantifies how much a gap (e.g., incomes by race) would close if we intervened to equalize a treatment (e.g., access to college). Drawing on causal decomposition analyses, this type of research question yields several benefits. First, gap-closing estimands place categories like race in a causal framework without making them play the role of the treatment (which is philosophically fraught for non-manipulable variables). Second, gap-closing estimands empower researchers to study disparities using new statistical and machine learning estimators designed for causal effects. Third, gap-closing estimands can directly inform policy: if we sampled from the population and actually changed treatment assignments, how much could we close gaps in outcomes? I provide open-source software (the R package gapclosing) to support these methods.

种族、性别和阶级的差异是描述性研究的重要目标。但理想情况下，研究不仅可以描述差异，还可以为缩小这些差距的干预措施提供信息。差距缩小估计量化了如果我们进行干预以平衡治疗（例如，进入大学），差距（例如，按种族划分的收入）将缩小多少。利用因果分解分析，这种类型的研究问题产生了几个好处。首先，缩小差距的估计将种族等类别置于因果框架中，而没有让它们扮演治疗的角色（这在哲学上对不可操纵的变量充满了担忧）。其次，差距缩小估计使研究人员能够使用为因果效应设计的新统计和机器学习估计量来研究差异。第三，缩小差距的估计可以直接为政策提供信息：如果我们从人群中抽样并实际改变治疗分配，我们可以在多大程度上缩小结果差距？我提供开源软件（R 包gapclosing ) 来支持这些方法。