Summary Propensity score matching is frequently used for estimating average treatment effects. Its applicability, however, is not confined to treatment evaluation. In this paper, it is shown that propensity score matching does not hinge on a selection on observables assumption and can be used to estimate not only adjusted means but also their distributions, even with non-i.i.d. sampling. Propensity score matching is used to analyze the gender wage gap among graduates in the UK. It is found that subject of degree contributes substantially to explaining the gender wage gap, particularly at higher quantiles of the wage distribution.