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Sharp bounds on causal effects under sample selection

abstract In many empirical problems, the evaluation of treatment effects is complicated by sample selection so 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 observed'' (whose outcomes are observed irrespective of the treatment). This paper complements those studies by considering further populations, namely the "compliers'' (whose outcomes are observed if they are treated) and the observed population. We derive sharp bounds under various assumptions (monotonicity and stochastic dominance) and provide an empirical application to a school voucher experiment.
   
type journal paper
   
keywords causal inference, principal stratification, nonparametric bounds, sample selection.
   
language English
kind of paper journal article
date of appearance 2014
journal Oxford Bulletin of Economics & Statistics
publisher Wiley-Blackwell (Oxford UK)
ISSN 0305-9049
ISSN (online) 1468-0084
DOI 10.1111/obes.12056
volume of journal 2014
number of issue forthcoming
page(s) 1-23
review double-blind review
   
citation Huber, M., & Mellace, G. (2014). Sharp bounds on causal effects under sample selection. Oxford Bulletin of Economics & Statistics, 2014(forthcoming), 1-23, DOI:10.1111/obes.12056.