Evidence map›Paper›PMID 40655227›Full record

ArticleAnnual review of statistics and its application2025

Identification and Inference with Invalid Instruments.

Hyunseung Kang, Zijian Guo, Zhonghua Liu, Dylan Small

Abstract read
In one paragraph

Article in Annual review of statistics and its application, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Hyunseung KangDepartment of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Zijian GuoDepartment of Statistics, Rutgers University, Piscataway, New Jersey, USA.
Zhonghua LiuDepartment of Biostatistics, Columbia University, New York, NY, USA.
Dylan SmallDepartment of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Novel Designs and Methods to Remove Hidden Confounding Bias in Health SciencesR01AG065276 · NIA · UNIVERSITY OF PENNSYLVANIA · PI TCHETGEN TCHETGEN, ERIC JOEL · 2020 to 2024
$2.4M
Robust Mendelian Randomization Framework with Multi-Omics Data for Alzheimer's Disease and Related DementiasR01AG086379 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Zhonghua Liu · 2024 to 2026
$1.6M
Semi-supervised Approaches to Denoising Electronic Health Records Data for Risk PredictionR01LM013614 · NLM · HARVARD UNIVERSITY D/B/A HARVARD SCHOOL OF PUBLIC HEALTH · PI CAI, TIANXI, GUO, ZIJIAN · 2021 to 2024
$1.4M
Predictive Modeling with High-Dimensional lncomplete DataR01GM140463 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI GUO, ZIJIAN · 2020 to 2022
$560k
NIA NIH HHS R01 AG065276NIA NIH HHS R01 AG086379NIGMS NIH HHS R01 GM140463NLM NIH HHS R01 LM013614
6 · The paper itself

Abstract

Instrumental variables (IVs) are widely used to study the causal effect of an exposure on an outcome in the presence of unmeasured confounding. IVs require an instrument, a variable that (

Indexed as

heteroskedasticityinstrumental variablesinvalid instrumentsMendelian randomization

Identifiers

PMID40655227
PMCPMC12253478

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.