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The Stata Journal
Volume 15 Number 2: pp. 411-436

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Bounding treatment effects: A command for the partial identification of the average treatment effect with endogenous and misreported treatment assignment

Ian McCarthy
Emory University
Atlanta, GA
[email protected]
 Daniel L. Millimet
Southern Methodist University
Dallas, TX
and IZA
Bonn, Germany
[email protected]
Manan Roy
University of North Carolina
Chapel Hill, NC
[email protected]
Abstract.  We present a new command, tebounds, that implements a variety of techniques to bound the average treatment effect of a binary treatment on a binary outcome in light of endogenous and misreported treatment assignment. To tighten the worst case bounds, the monotone treatment selection, monotone treatment response, and monotone instrumental-variable assumptions of Manski and Pepper (2000, Econometrica 68: 997–1010), Kreider and Pepper (2007, Journal of the American Statistical Association 102: 432–441), Kreider et al. (2012, Journal of the American Statistical Association 107: 958–975), and Gundersen, Kreider, and Pepper (2012, Journal of Econometrics 166: 79–91) may be imposed. Imbens–Manski confidence intervals are provided.
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View all articles with these keywords: tebounds, treatment effects, selection, misreporting, monotone instrumental variable, monotone treatment selection, monotone treatment response, partial identification, set identification

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