Adding covariates to bounds: what is the question?

Jonzon Gustav (), Gabriel Erin E. (), Sjölander Arvid () and Sachs Michael C. ()
Additional contact information
Jonzon Gustav: Department of Medical Epidemiology and Biostatistics, 27106 Karolinska Institutet , Stockholm, Sweden
Gabriel Erin E.: Section of Biostatistics, Department of Public Health and the Pioneer Centre for SMARTbiomed, University of Copenhagen, Copenhagen, Denmark
Sjölander Arvid: Department of Medical Epidemiology and Biostatistics, 27106 Karolinska Institutet , Stockholm, Sweden
Sachs Michael C.: Section of Biostatistics, Department of Public Health and the Pioneer Centre for SMARTbiomed, University of Copenhagen, Copenhagen, Denmark

Journal of Causal Inference, 2025, vol. 13, issue 1, 15

Abstract: Symbolic nonparametric bounds for partial identification of causal effects now have a long history in the causal literature. Sharp bounds, bounds that use all available information to make the range of values as narrow as possible, are often the goal. For this reason, many publications have focused on deriving sharp bounds, but the concept of sharp bounds is nuanced and can be misleading. In settings with ancillary covariates, the situation becomes more complex. We provide clear definitions for pointwise and uniform sharpness of covariate-conditional bounds, that we then use to prove some general and some specific to the instrumental variable (IV) setting results about the relationship between these two concepts. As we demonstrate, general conditions are much more difficult to determine and thus, we urge authors to be clear when including ancillary covariates in bounds via conditioning about the setting of interest and the assumptions made.

Keywords: partial identification; causal bounds; instrumental variables (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2025-0004 (text/html)

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:bpj:causin:v:13:y:2025:i:1:p:15:n:1002

DOI: 10.1515/jci-2025-0004

Access Statistics for this article

Journal of Causal Inference is currently edited by Elias Bareinboim, Jin Tian and Iván Díaz

More articles in Journal of Causal Inference from De Gruyter
Bibliographic data for series maintained by Peter Golla ().