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LEEBOUNDS: Lee's Treatment Effect Bounds for Samples with Partially Non-Random Sample Selection for Stata 

Even if assignment of treatment is purely exogenous, estimated treatment effects may suffer from severe bias, if the available sample is subject to partially non-random sample selection or partially non-random sample attrition. To address this issue non-parametrically, Lee (2009) proposes an estimator for treatment effect bounds. 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 Heckman (1979) estimator, Lee (2009) bounds rest on very few assumptions, i.e. random assignment of treatment and monotonicity. The latter means that the treatment status affects selection in just one direction. That is, receipt of treatment makes selection either more or less likely for any observation. We introduce the new Stata command LEEBOUNDS that implements Lee's bounds estimator in Stata. The commands allow for several options, such as tightening bounds by the use of covariates and statistical inference based on a weighted bootstrap.


Author: Harald Tauchmann (Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI), Essen)

Download: leebounds.ado

Download: leebounds.sthlp