Evidence map›Paper›PMID 42370200›Full record

ArticleProceedings of the ... Conference on Fairness, Accountability, and Transparency2026

A pipeline for enabling path-specific causal fairness in observational health data.

Aparajita Kashyap, Sara Matijevic, Noémie Elhadad, Steven A Kushner, Shalmali Joshi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the ... Conference on Fairness, Accountability, and Transparency, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Aparajita KashyapDepartment of Biomedical Informatics, Columbia University, USA.
Sara MatijevicNuffield Department of Women's and Reproductive Health, University of Oxford, UK.
Noémie ElhadadDepartment of Biomedical Informatics, Columbia University, USA.
Steven A KushnerDepartment of Psychiatry, Columbia University, USA.
Shalmali JoshiDepartment of Biomedical Informatics, Columbia University, USA.

Funding

Training in Biomedical Informatics at Columbia UniversityT15LM007079 · NLM · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI NOEMIE ELHADAD, GEORGE M HRIPCSAK · 1992 to 2026
$28.9M
1/5 Clinical Outcome Prediction of Psychosis from EHRs (COPPER)R01MH137679 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI Paul Stuart Appelbaum, NOEMIE ELHADAD · 2024 to 2026
$3.0M
2/5 Clinical Outcome Prediction of Psychosis from Electronic Health Records (COPPER)R01MH137676 · NIMH · NEW YORK GENOME CENTER · PI MICHAEL C ZODY · 2024 to 2026
$2.0M
NIMH NIH HHS R01 MH137676NIMH NIH HHS R01 MH137679NLM NIH HHS T15 LM007079
6 · The paper itself

Abstract

When training machine learning (ML) models for potential deployment in a healthcare setting, it is essential to ensure that they do not replicate or exacerbate existing healthcare biases. Although many definitions of fairness exist, we focus on

Indexed as

Causal fairnesscausal inferencefair machine learningfoundation modelshealthcareobservational health data

Identifiers

PMID42370200
PMCPMC13309187

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.