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Nonrandom sample selection may render estimated treatment effects biased even if assignment of treatment is purely random. Lee (2009, Review of Economic Studies, 76: 1071–1102) proposes an estimator for treatment-effect bounds that limit the possible range of the treatment effect. In this approach, the lower and upper bound correspond to extreme assumptions about the missing information that are consistent with the observed data. In contrast to conventional parametric approaches to correcting for sample-selection bias, Lee’s bounds estimator rests on very few assumptions. I introduce the new command leebounds, which implements the estimator in Stata. The command allows for several options, such as tightening bounds by using covariates.
