Lead water service lines are all over the city. But majority Black and Latino neighborhoods bear the biggest burden, our analysis finds.

Can we afford the consumption patterns of the super-rich? Or does human and planetary wellbeing require us to drastically reduce inequality?

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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 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 adult income between the children of high- vs. low-income parents). Empirically, we demonstrate a previously undiscovered role played by the new selection component in intergenerational income persistence.

Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As...