Novel bounds for causal effects based on sensitivity parameters on the risk difference scale

Sjölander Arvid () and Hössjer Ola
Additional contact information
Sjölander Arvid: Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
Hössjer Ola: Department of Mathematics, Stockholm University, Stockholm, Sweden

Journal of Causal Inference, 2021, vol. 9, issue 1, 190-210

Abstract: Unmeasured confounding is an important threat to the validity of observational studies. A common way to deal with unmeasured confounding is to compute bounds for the causal effect of interest, that is, a range of values that is guaranteed to include the true effect, given the observed data. Recently, bounds have been proposed that are based on sensitivity parameters, which quantify the degree of unmeasured confounding on the risk ratio scale. These bounds can be used to compute an E-value, that is, the degree of confounding required to explain away an observed association, on the risk ratio scale. We complement and extend this previous work by deriving analogous bounds, based on sensitivity parameters on the risk difference scale. We show that our bounds can also be used to compute an E-value, on the risk difference scale. We compare our novel bounds with previous bounds through a real data example and a simulation study.

Keywords: causal inference; bounds; sensitivity analysis; E-value (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/jci-2021-0024 (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:9:y:2021:i:1:p:190-210:n:10

DOI: 10.1515/jci-2021-0024

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 ().