Lee (2009) treatment-effect bounds for nonrandom sample selection

Harald Tauchmann

Stata Journal, 2014, vol. 14, issue 4, 884-894

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. Copyright 2014 by StataCorp LP.

Keywords: leebounds; nonparametric; randomized trial; sample selection; attrition; bounds; treatment effect (search for similar items in EconPapers)
Date: 2014
Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj14-4/st0364/
References: Add references at CitEc
Citations: View citations in EconPapers (25)

Downloads: (external link)
http://www.stata-journal.com/article.html?article=st0364 link to article purchase

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tsj:stataj:v:14:y:2014:i:4:p:884-894

Ordering information: This journal article can be ordered from
http://www.stata-journal.com/subscription.html

Access Statistics for this article

Stata Journal is currently edited by Nicholas J. Cox and Stephen P. Jenkins

More articles in Stata Journal from StataCorp LLC
Bibliographic data for series maintained by Christopher F. Baum () and Lisa Gilmore ().