Evidence map›Paper›PMID 39380507›Full record

ArticleStatistical methods in medical research2024

Comparison of random forest methods for conditional average treatment effect estimation with a continuous treatment.

Sami Tabib, Denis Larocque

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Statistical methods in medical research, 2024. 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

2 authors.

Sami TabibDepartment of Decision Sciences, HEC Montréal, Montréal, Canada.
Denis LarocqueDepartment of Decision Sciences, HEC Montréal, Montréal, Canada.ORCID 0000-0002-7372-7943

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We are addressing the problem of estimating conditional average treatment effects with a continuous treatment and a continuous response, using random forests. We explore two general approaches: building trees with a split rule that seeks to increase the heterogeneity of the treatment effect estimation and building trees to predict

Indexed as

Models, StatisticalComputer SimulationHumansRandom ForestTreatment Outcomecausal modelingcolliding effectConditional average treatment effect (CATE)confounding effectcontinuous treatmentensemble methodincremental modelinglocal centeringrandom foresttree-based methoduplift modeling

Identifiers

PMID39380507
PMCPMC11577706

What OpenQuestion holds

Textmetadata
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.