Evidence map›Paper›PMID 42781639›Full record

ArticleObservational studies2026

Getting to Know Worst-Case Confounders.

Melody Huang, Samuel D Pimentel

Abstract read
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

2 authors.

Melody HuangDepartment of Political Science and Statistics & Data ScienceYale UniversityNew Haven, CT, USA.
Samuel D PimentelDepartment of StatisticsUniversity of California, BerkeleyBerkeley, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since Cornfield, Haenszel, Hammond, Lilienfeld, Shimkin, and Wynder (1959)'s seminal contribution, many sensitivity analyses have been introduced. Modern-day approaches to sensitivity analysis aim to impose relatively little structure on the unobserved confounder. However, while a sensitivity analysis may not rely on many assumptions on the unobserved confounder, the worst-case bounds that are solved for within a sensitivity analysis are often only realized when a hypothetical confounder takes on a specific form, which is not always made explicit. In the following commentary, we consider several leading sensitivity analysis approaches and review what is known about the structure of the confounder at which the worst-case bounds are realized. We show that the type of sensitivity model used can induce different data generating processes for the worst-case confounders. We argue that understanding the structure of the omitted confounder implied by the worst-case bounds is crucial for more transparent sensitivity analysis, and we suggest directions for future work.

Indexed as

Causal inferenceCornfieldSensitivity analysis

Identifiers

PMID42781639
PMCPMC13600455

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.