Evidence map›Paper›PMID 36970634›Full record

ArticleCEUR workshop proceedings2022

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design.

Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Margo Seltzer

Abstract read
In one paragraph

Article in CEUR workshop proceedings, 2022. 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

4 authors.

Ali BehrouzUniversity of British Columbia Vancouver, British Columbia, Canada.
Mathias LécuyerUniversity of British Columbia Vancouver, British Columbia, Canada.
Cynthia RudinDuke University Durham, North Carolina, USA.
Margo SeltzerUniversity of British Columbia Vancouver, British Columbia, Canada.

Funding

A machine learning framework for understanding impacts on the HIV latent reservoir size, including drugs of abuseR01DA054994 · NIDA · DUKE UNIVERSITY · PI RUDIN, CYNTHIA · 2021 to 2025
$2.3M
NIDA NIH HHS R01 DA054994
6 · The paper itself

Abstract

Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for

Indexed as

ExplainabilityInterpretable Machine LearningOptimal Sparse Decision TreesOptimal Treatment Regimes

Identifiers

PMID36970634
PMCPMC10039433

What OpenQuestion holds

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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.