We propose a nonparametric framework that decomposes the causal contributions of a treatment variable to an outcome disparity between two groups. We decompose the causal contributions of treatment into group differences in 1) treatment prevalence, 2) average treatment effects, and 3) selection into treatment based on individual-level treatment effects. Our framework reformulates the classic Kitagawa-Blinder-Oaxaca decomposition nonparametrically in causal terms, complements causal mediation analysis by explaining group disparities instead of group effects, and distinguishes more mechanisms than recent random equalization decomposition. In contrast to all prior approaches, our framework isolates the causal contribution of differential selection into treatment as a novel mechanism for explaining and ameliorating group disparities. We develop nonparametric estimators based on efficient influence functions that are $\sqrt{n}$-consistent, asymptotically normal, semiparametrically efficient, and multiply robust to misspecification. We apply our framework to decompose the causal contributions of education to the disparity in adult income between parental income groups (intergenerational income persistence). We find that both differential prevalence of, and differential selection into, college graduation significantly contribute to intergenerational income persistence.

翻译：我们提出了一种非参数化框架，用于将处理变量对两组之间结果差异的因果贡献进行分解。我们将处理的因果贡献分解为群体在以下三个方面的差异：1）处理普遍性，2）平均处理效应，3）基于个体层面处理效应的选择进入处理。我们的框架以因果术语对经典的Kitagawa-Blinder-Oaxaca分解进行了非参数化重构，通过解释群体差异而非群体效应来补充因果中介分析，并且比近期随机均衡分解区分了更多机制。与所有先前方法不同，我们的框架将差异化的选择进入处理的因果贡献作为解释和改善群体差异的新机制单独剥离。我们基于有效影响函数开发了非参数估计量，这些估计量具有√n一致性、渐近正态性、半参数有效性，并对模型误设具有多重鲁棒性。我们将该框架应用于分解教育对父母收入群体间成年收入差异（代际收入持续性）的因果贡献。我们发现，大学毕业生在普遍性上的差异以及选择进入的差异均对代际收入持续性产生显著影响。