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Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives
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.
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