We introduce a new nonparametric causal decomposition approach that identifies the mechanisms by which a treatment variable contributes to a group-based outcome disparity. Our approach distinguishes three mechanisms: group differences in 1) treatment prevalence, 2) average treatment effects, and 3) selection into treatment based on individual-level treatment effects. Our approach reformulates classic Kitagawa-Blinder-Oaxaca decompositions in causal and nonparametric terms, complements causal mediation analysis by explaining group disparities instead of group effects, and isolates conceptually distinct mechanisms conflated in recent random equalization decompositions. In contrast to all prior approaches, our framework uniquely identifies differential selection into treatment as a novel disparity-generating mechanism. Our approach can be used for both the retrospective causal explanation of disparities and the prospective planning of interventions to change disparities. We present both an unconditional and a conditional decomposition, where the latter quantifies the contributions of the treatment within levels of certain covariates. We develop nonparametric estimators that are $\sqrt{n}$-consistent, asymptotically normal, semiparametrically efficient, and multiply robust. We apply our approach to analyze the mechanisms by which college graduation causally contributes to intergenerational income persistence (the disparity in income attainment between parental income groups).

翻译：本文提出了一种新的非参数因果分解方法，用于识别处理变量如何通过不同机制影响基于群体的结果差异。我们的方法区分了三种机制：1) 处理普及率的群体差异；2) 平均处理效应的群体差异；3) 基于个体层面处理效应的选择性差异。该方法以因果和非参数的形式重构了经典的Kitagawa-Blinder-Oaxaca分解，通过解释群体差异而非群体效应来补充因果中介分析，并分离了近期随机均衡分解中混淆的概念性机制。与所有现有方法相比，我们的框架首次将差异化的处理选择识别为新的差异生成机制。该方法既可用于差异的回顾性因果解释，也可用于改变差异的前瞻性干预规划。我们提出了无条件分解和条件分解两种形式，其中条件分解可量化特定协变量水平下处理的贡献度。我们开发了具有$\sqrt{n}$一致性、渐近正态性、半参数有效性和多重稳健性的非参数估计量。我们将该方法应用于分析大学毕业生如何通过因果机制影响代际收入持续性（不同父母收入群体间收入获取能力的差异）。