Evidence map›Paper›PMID 29387137›Full record

ArticleEmerging themes in epidemiology2018

An introduction to instrumental variable assumptions, validation and estimation.

Mette Lise Lousdal

Abstract read
In one paragraph

Article in Emerging themes in epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
73citing papers in PubMed, 4 pooled it
–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

73 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Trial
  6. Canon enables causal inference of downstream genes in single-cell CRISPR studies via instrumental variable analysis.Proceedings of the National Academy of Sciences of the United States of America · 2026
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  14. Transcriptome-wide root causal inference.PLoS computational biology · 2025
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  16. Causal association between PMScientific reports · 2025
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13 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

1 author.

Mette Lise LousdalDepartment of Public Health, Aarhus University, Bartholins Allé 2, 8000 Aarhus C, Denmark.ORCID 0000-0001-9762-1927

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The instrumental variable method has been employed within economics to infer causality in the presence of unmeasured confounding. Emphasising the parallels to randomisation may increase understanding of the underlying assumptions within epidemiology. An instrument is a variable that predicts exposure, but conditional on exposure shows no independent association with the outcome. The random assignment in trials is an example of what would be expected to be an ideal instrument, but instruments can also be found in observational settings with a naturally varying phenomenon e.g. geographical variation, physical distance to facility or physician's preference. The fourth identifying assumption has received less attention, but is essential for the generalisability of estimated effects. The instrument identifies the group of

Indexed as

Causal inferenceExchangeabilityInstrumental variableMonotonicityRandomizationUnmeasured confounding

Identifiers

PMID29387137
PMCPMC5776781

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.