Evidence map›Paper›PMID 42781669›Full record

ArticleObservational studies2026

Sensitivity Analysis of the Consistency Assumption.

Brian Knaeble, Qinyun Lin, Erich Kummerfeld, Kenneth A Frank

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Observational studies, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Brian KnaebleDepartment of Computer ScienceUtah Valley UniversityOrem, UT, USA.
Qinyun LinSchool of Public Health and Community MedicineUniversity of GothenburgSweden.
Erich KummerfeldInstitute for Health InformaticsUniversity of MinnesotaMinneapolis, MN, USA.
Kenneth A FrankDepartment of Counseling, Educational Psychology, and Special EducationMichigan State UniversityEast Lansing, MI, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Causal inferenceConfoundingConsistencyFiltered probability spacesHidden versionsPartial identificationPotential outcomesSensitivity analysisStable unit treatment value assumption (SUTVA)Stochastic counterfactuals

Identifiers

PMID42781669
PMCPMC13600454

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.