leebounds: Lee’s treatment effect bounds for samples with nonrandom sample selection

Even if assignment of treatment is purely exogenous, estimating treatment effects may suffer from severe bias if the available sample is subject to nonrandom sample selection/attrition. Lee (Review of Economic Studies, 2009) addresses this issue by proposing an estimator for treatment effect bounds in the presence of nonrandom sample selection. In this approach, the lower and upper bound, respectively, correspond to extreme assumptions about the missing information that are consistent with the observed data. As opposed to conventional parametric approaches to correcting for sample selection bias, such as the classical heckit estimator, Lee bounds rest on very few assumptions, namely, random assignment of treatment and monotonicity. The latter means that treatment affects selection for any individual in the same direction. I introduce the new Stata command leebounds, which implements Lee’s bounds estimator in Stata. The command allows for several options, such as tightening bounds by the use of covariates, confidence intervals for the treatment effect, and statistical inference based on a weighted bootstrap. The command is applied to data gathered from a randomized trial of the effect of financial incentives on weight-loss among obese individuals.