Unconditional and conditional quantile treatment effect: Identification strategies and interpretations

Abstract

This paper reviews strategies that allow one to identify the effects of policy interventions on the unconditional or conditional distribution of the outcome of interest. This distinction is irrelevant when one focuses on average treatment effects since identifying assumptions typically do not affect the parameter’s interpretation. Conversely, finding the appropriate answer to a research question on the effects over the distribution requires particular attention in the choice of the identification strategy. Indeed, quantiles of the conditional and unconditional distribution of a random variable carry a different meaning even if identification of both these set of parameters may require conditioning on observed covariates.