Evidence map›Paper›PMID 34121051›Full record

ArticleJournal of epidemiology2021

Using Propensity Scores for Causal Inference: Pitfalls and Tips.

Koichiro Shiba, Takuya Kawahara

Abstract read
In one paragraph

Article in Journal of epidemiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing 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

70 citing papers in PubMed.

  1. Trial
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  11. The Relationship Between Depression and Coping Insight Dimensions in the Transition to Motherhood.Stress and health : journal of the International Society for the Investigation of Stress · 2026
    Article
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  15. Article
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  18. Observational
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  20. Observational

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

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

2 authors.

Koichiro ShibaDepartment of Epidemiology, Harvard T.H. Chan School of Public Health.ORCID http://orcid.org/0000-0001-7956-6485
Takuya KawaharaClinical Research Promotion Center, The University of Tokyo Hospital.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Methods based on propensity score (PS) have become increasingly popular as a tool for causal inference. A better understanding of the relative advantages and disadvantages of the alternative analytic approaches can contribute to the optimal choice and use of a specific PS method over other methods. In this article, we provide an accessible overview of causal inference from observational data and two major PS-based methods (matching and inverse probability weighting), focusing on the underlying assumptions and decision-making processes. We then discuss common pitfalls and tips for applying the PS methods to empirical research and compare the conventional multivariable outcome regression and the two alternative PS-based methods (ie, matching and inverse probability weighting) and discuss their similarities and differences. Although we note subtle differences in causal identification assumptions, we highlight that the methods are distinct primarily in terms of the statistical modeling assumptions involved and the target population for which exposure effects are being estimated.

Indexed as

CausalityPropensity ScoreHumansModels, Statisticalinverse probability weightingmatchingpropensity scoretarget population

Identifiers

PMID34121051
PMCPMC8275441

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.