Using negative controls to identify causal effects with invalid instrumental variables.

O Dukes, D B Richardson, Z Shahn, J M Robins, E J Tchetgen Tchetgen

Abstract read

In one paragraph

Article in Biometrika, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing 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

1 citing paper in PubMed.

Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies.Statistics in medicine · 2026
Article

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

5 authors.

O DukesDepartment of Applied Mathematics, Statistics and Computer Science, Ghent University, Krijgslaan 281 S9, 9000 Ghent, Belgium.ORCID https://orcid.org/0000-0002-9145-3325

D B RichardsonDepartment of Environmental and Occupational Health, University of California Irvine, 653 E. Peltason Drive, Irvine, California 92697, USA.

Z ShahnDepartment of Epidemiology and Biostatistics, City University of New York, 55 W 125th St, New York, New York 10027, USA.

J M RobinsDepartment of Epidemiology, Harvard T. H. Chan School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, USA.

E J Tchetgen TchetgenDepartment of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, Philadelphia, Pennsylvania 19104, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many proposals for the identification of causal effects require an instrumental variable that satisfies strong, untestable unconfoundedness and exclusion restriction assumptions. In this paper, we show how one can potentially identify causal effects under violations of these assumptions by harnessing a negative control population or outcome. This strategy allows one to leverage subpopulations for whom the exposure is degenerate, and requires that the instrument-outcome association satisfies a certain parallel trend condition. We develop semiparametric efficiency theory for a general instrumental variable model, and obtain a multiply robust, locally efficient estimator of the average treatment effect in the treated. The utility of the estimators is demonstrated in simulation studies and an analysis of the Life Span Study.

Indexed as

Causal inferenceSemiparametric theoryUnmeasured confounding

Identifiers

PMID40046718

DOI10.1093/biomet/asae064

PMCPMC11878522

What Socratic holds

Textmetadata

Read underepoch 390

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 Socratic graph.