Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/283960 
Authors: 
Year of Publication: 
2023
Series/Report no.: 
Working Paper No. 2023-5
Publisher: 
University of Massachusetts, Department of Economics, Amherst, MA
Abstract: 
Covariate benchmarking is an important part of sensitivity analysis about omitted variable bias and can be used to bound the strength of the unobserved confounder using information and judgments about observed covariates. It is common to carry out formal covariate benchmarking under the assumption that the unobserved confounder is orthogonal to the observed covariates. This assumption is restrictive and will be difficult to defended in most empirical analyses. In this paper I show that relaxing the orthogo- nality assumption leads to a breakdown of a recently proposed innovative formal covariate benchmarking methodology.
Subjects: 
confounding
omitted variable bias
sensitivity analysis
JEL: 
C01
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Working Paper

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