Sensitivity Analysis of the Consistency Assumption
This addresses a specific issue in causal inference for researchers dealing with treatments like surgery where hidden factors (e.g., surgeon skill) can bias results, representing an incremental advance in sensitivity analysis methodology.
The paper tackles the problem of hidden versions of treatment violating the consistency assumption in causal inference by developing a new sensitivity analysis method focused on confounding by hidden versions, rather than unmeasured covariates, with example applications provided.
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 of 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.