Evidence map›Paper›PMID 24599889›Full record

ArticleStatistics in medicine2014

Instrumental variable methods for causal inference.

Michael Baiocchi, Jing Cheng, Dylan S Small

Erratum issuedAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistics in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 281 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
281citing papers in PubMed, 6 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

281 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  20. Genetic analysis of imaging-derived phenotypes.Nature reviews. Genetics · 2026
    Review

221 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Michael BaiocchiDepartment of Statistics, Stanford University, Stanford, CA, U.S.A.
Jing Cheng
Dylan S Small

Funding

Statistics and Data Coordinating CenterU54DE019285 · NIDCR · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GANSKY, STUART A · 2008 to 2014
$24.9M
Instrumental Variable Methods for Longitudinal Discrete DataRC4MH092722 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI JOFFE, MARSHALL M · 2010 to 2010
$1.5M
NIDCR NIH HHS U54 DE019285NIDCR NIH HHS U54DE019285NIMH NIH HHS RC4 MH092722NIMH NIH HHS RC4MH092722
6 · The paper itself

Abstract

A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment, and instead, an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration, which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies.

Indexed as

CausalityConfounding Factors, EpidemiologicData Interpretation, StatisticalHumansInfant, NewbornObservational Studies as TopicOutcome Assessment, Health Carecomparative effectivenessconfoundinginstrumental variablesobservational study

Identifiers

PMID24599889
PMCPMC4201653

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.