Evidence map›Paper›PMID 40809738›Full record

ArticleJournal of causal inference2025

Role of placebo samples in observational studies.

Ting Ye, Qijia He, Shuxiao Chen, Bo Zhang

Abstract read
In one paragraph

Article in Journal of causal inference, 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.

  1. 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.

Ting YeDepartment of Biostatistics, University of Washington.
Qijia HeDepartment of Statistics, University of Washington.
Shuxiao ChenDepartment of Statistics and Data Science, The Wharton School, University of Pennsylvania.
Bo ZhangVaccine and Infectious Disease Division, Fred Hutchinson Cancer Center.

Funding

Advancing Causal Inference in Integrative Omics AnalysisR35GM155070 · NIGMS · UNIVERSITY OF WASHINGTON · PI Ting Ye · 2024 to 2026
$1.1M
NIGMS NIH HHS R35 GM155070
6 · The paper itself

Abstract

In an observational study, it is common to leverage known null effects to detect bias. One such strategy is to set aside a placebo sample - a subset of data immune from the hypothesized cause-and-effect relationship. Existence of an effect in the placebo sample raises concerns about unmeasured confounding bias while absence of it helps corroborate the causal conclusion. This paper describes a framework for using a placebo sample to detect and remove bias. We state the identification assumptions and develop estimation and inference methods based on outcome regression, inverse probability weighting, and doubly-robust approaches. Simulation studies investigate the finite-sample performance of the proposed methods. We illustrate the methods using an empirical study of the effect of the earned income tax credit on infant health.

Indexed as

62D20Causal inferencePlacebo testTreatment effect heterogeneityUnmeasured confounding

Identifiers

PMID40809738
PMCPMC12345972

What OpenQuestion holds

Textmetadata
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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.