  This paper proposes a new class of heterogeneous causal quantities, named
\textit{outcome conditioned} average structural derivatives (OASD) in a general
nonseparable model. OASD is the average partial effect of a marginal change in
a continuous treatment on the individuals located at different parts of the
outcome distribution, irrespective of individuals' characteristics. OASD
combines both features of ATE and QTE: it is interpreted as straightforwardly
as ATE while at the same time more granular than ATE by breaking the entire
population up according to the rank of the outcome distribution.
  One contribution of this paper is that we establish some close relationships
between the \textit{outcome conditioned average partial effects} and a class of
parameters measuring the effect of counterfactually changing the distribution
of a single covariate on the unconditional outcome quantiles. By exploiting
such relationship, we can obtain root-$n$ consistent estimator and calculate
the semi-parametric efficiency bound for these counterfactual effect
parameters. We illustrate this point by two examples: equivalence between OASD
and the unconditional partial quantile effect (Firpo et al. (2009)), and
equivalence between the marginal partial distribution policy effect (Rothe
(2012)) and a corresponding outcome conditioned parameter.
  Because identification of OASD is attained under a conditional exogeneity
assumption, by controlling for a rich information about covariates, a
researcher may ideally use high-dimensional controls in data. We propose for
OASD a novel automatic debiased machine learning estimator, and present
asymptotic statistical guarantees for it. We prove our estimator is root-$n$
consistent, asymptotically normal, and semiparametrically efficient. We also
prove the validity of the bootstrap procedure for uniform inference on the OASD
process.
