Evidence map›Paper›PMID 38412300›Full record

ArticleBiometrics2024

Using instrumental variables to address unmeasured confounding in causal mediation analysis.

Kara E Rudolph, Nicholas Williams, Iván Díaz

Abstract read
In one paragraph

Article in Biometrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.Annual review of statistics and its application · 2025
    Article
  3. When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022
    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

3 authors.

Kara E RudolphDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York 10032, USA.ORCID 0000-0002-9417-7960
Nicholas WilliamsDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York 10032, USA.
Iván DíazDivision of Biostatistics, New York University Grossman School of Medicine, New York, New York 10016, USA.

Funding

Mechanisms linking neighborhood poverty to problematic adolescent drug useR00DA042127 · NIDA · UNIVERSITY OF CALIFORNIA AT DAVIS · PI RUDOLPH, KARA ELIZABETH · 2018 to 2021
$889k
NIDA NIH HHS R00 DA042127
6 · The paper itself

Abstract

Mediation analysis is a strategy for understanding the mechanisms by which interventions affect later outcomes. However, unobserved confounding concerns may be compounded in mediation analyses, as there may be unobserved exposure-outcome, exposure-mediator, and mediator-outcome confounders. Instrumental variables (IVs) are a popular identification strategy in the presence of unobserved confounding. However, in contrast to the rich literature on the use of IV methods to identify and estimate a total effect of a non-randomized exposure, there has been almost no research into using IV as an identification strategy to identify mediational indirect effects. In response, we define and nonparametrically identify novel estimands-double complier interventional direct and indirect effects-when 2, possibly related, IVs are available, one for the exposure and another for the mediator. We propose nonparametric, robust, efficient estimators for these effects and apply them to a housing voucher experiment.

Indexed as

Mediation AnalysisConfounding Factors, Epidemiologiccausal inferenceefficient influence functioninstrumental variableinterventional indirect effectmediationnatural indirect effect

Identifiers

PMID38412300
PMCPMC11057970

What OpenQuestion holds

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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.