  This paper proposes a new class of distributional causal quantities, referred
to as the \textit{outcome conditioned partial policy effects} (OCPPEs), to
measure the \textit{average} effect of a general counterfactual intervention of
a target covariate on the individuals in different quantile ranges of the
outcome distribution.
  The OCPPE approach is valuable in several aspects: (i) Unlike the
unconditional quantile partial effect (UQPE) that is not $\sqrt{n}$-estimable,
an OCPPE is $\sqrt{n}$-estimable. Analysts can use it to capture heterogeneity
across the unconditional distribution of $Y$ as well as obtain accurate
estimation of the aggregated effect at the upper and lower tails of $Y$. (ii)
The semiparametric efficiency bound for an OCPPE is explicitly derived. (iii)
We propose an efficient debiased estimator for OCPPE, and provide feasible
uniform inference procedures for the OCPPE process. (iv) The efficient doubly
robust score for an OCPPE can be used to optimize infinitesimal nudges to a
continuous treatment by maximizing a quantile specific Empirical Welfare
function. We illustrate the method by analyzing how anti-smoking policies
impact low percentiles of live infants' birthweights.
