Lee’s treatment effect bounds for non-random sample selection - an implementation in Stata
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Date
2013-10-02
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Abstract
Non-random sample selection may render estimated treatment effects
biased even if assignment of treatment is purely random. Lee (2009) proposes an
estimator for treatment effect bounds that limit the possible range of the treatment
effect. In this approach, the lower and upper bound, respectively, correspond to
extreme assumptions about the missing information, which are consistent with the
observed data. As opposed to conventional parametric approaches to correcting
for sample selection bias, Lee's bounds estimator rests on very few assumptions.
We introduce the new Stata command leebounds that implements the estimator
in Stata. The command allows for several options, such as tightening bounds by
the use of covariates.
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Keywords
attrition, bounds, non-parametric, randomized trial, sample selection, treatment effect