Abstract
An empirical approach for the analysis of treatment effect at various quantiles in the case of multiple treatment conditions is here proposed. Outcome changes under multiple treatment conditions are computed using (a) inverse propensity score weights and (b) unconditional outcome distribution within each group. Through (a) and (b), the standard double robust estimator is extended to evaluate treatment effect not only on average but also in the tails (quantiles). A Monte Carlo study designed to examine and assess the performance of the proposed approach and two empirical applications conclude the analysis.
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