  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).
