Evidence map›Paper›PMID 42078562›Full record

ArticleJournal of the Royal Statistical Society. Series B, Statistical methodology2026

Causal K-Means Clustering.

Kwangho Kim, Jisu Kim, Edward H Kennedy

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of the Royal Statistical Society. Series B, Statistical methodology, 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

3 authors.

Kwangho KimDepartment of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea.
Jisu KimDepartment of Statistics, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Korea.
Edward H KennedyDepartment of Statistics and Data Science, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA 15213, USA.

Funding

Efficient nonparametric estimation of heterogeneous treatment effects in causal inferenceR01LM013361 · NLM · UNIVERSITY OF PENNSYLVANIA · PI KEELE, LUKE, KENNEDY, EDWARD H. · 2021 to 2025
$2.0M
NLM NIH HHS R01 LM013361
6 · The paper itself

Abstract

Causal effects are often characterized with population summaries. These might provide an incomplete picture when there are heterogeneous treatment effects across subgroups. Since the subgroup structure is typically unknown, it is more challenging to identify and evaluate subgroup effects than population effects. We propose a new solution to this problem:

Indexed as

Causal inferenceHeterogeneous treatment effectObservational studiesPersonalizationSubgroup analysis

Identifiers

PMID42078562
PMCPMC13132527

What OpenQuestion holds

Textmetadata
LicenceTDM
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.