Sharp bounds on causal effects under sample selection

Abstract

In many empirical problems, the evaluation of treatment effects is complicated by sample selection such that the outcome is only observed for a non-random subpopulation. In the absence of instruments and/or tight parametric assumptions, treatment effects are not point identified, but can be bounded under mild restrictions. Previous work on partial identification has primarily focused on the "always selected" (whose outcomes are observed irrespective of the treatment). This paper complements those studies by considering further populations, namely the "compliers" (whose selection states react to the treatment) and the selected population. We derive sharp bounds under various assumptions (monotonicity and stochastic dominance) and provide an empirical application to a school voucher experiment.Causal inference, principal stratification, nonparametric bounds, sample selection