Lee (2009) treatment-effect bounds for nonrandom sample selection
Harald Tauchmann
University of Erlangen-Nuremberg,
Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI),
and Centre of Health Economics Research (CINCH)
Nürnberg, Germany
[email protected]
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Abstract. 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.
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Harald Tauchmann
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leebounds, nonparametric, randomized trial, sample selection, attrition, bounds, treatment effect
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