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Updated: Sep 26, 2026

Data Acquisition Protocol for Determining Embedded Sensitivity Functions
Published on: April 20, 2016
Sensitivity Analysis of the Consistency Assumption
Brian Knaeble1, Qinyun Lin2, Erich Kummerfeld3
1Department of Computer ScienceUtah Valley UniversityOrem, UT, USA.
Abstract:
Sensitivity analysis informs causal inference by assessing the sensitivity of conclusions to departures from assumptions. The consistency assumption states that there are no hidden versions of treatment and that the outcome arising naturally equals the outcome arising from intervention. When reasoning about the possibility of consistency violations, it can be helpful to distinguish between covariates and versions of treatment. In the context of surgery, for example, genomic variables are covariates and the skill of a particular surgeon is a version of treatment. There may be hidden versions of treatment, and this paper addresses that concern with a new kind of sensitivity analysis. Whereas many methods for sensitivity analysis are focused on confounding by unmeasured covariates, the methodology in this paper is focused on confounding by hidden versions of treatment. In this paper, new mathematical notation is introduced to support the novel method, and example applications are described.
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