Evidence map›Paper›PMID 38323299›Full record

ArticleJournal of causal inference2022

Individualized treatment rules under stochastic treatment cost constraints.

Hongxiang Qiu, Marco Carone, Alex Luedtke

Abstract read
In one paragraph

Article in Journal of causal inference, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. MOSIC: Model-Agnostic Optimal Subgroup Identification with Multi-Constraint for Improved Reliability.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2026
    Article
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.

Hongxiang QiuDepartment of Statistics, the Wharton School, University of Pennsylvania.
Marco CaroneDepartment of Biostatistics, University of Washington.
Alex LuedtkeDepartment of Statistics, University of Washington.

Funding

Statistical Methods for Incorporating Machine Learning Tools in Inference and Large-Scale Surveillance using Electronic Medical Records DataR01HL137808 · NHLBI · UNIVERSITY OF WASHINGTON · PI CARONE, MARCO · 2019 to 2023
$2.4M
High-Resolution Inference for Correlates of Vaccine ProtectionDP2LM013340 · NLM · UNIVERSITY OF WASHINGTON · PI LUEDTKE, ALEX · 2019 to 2019
$2.3M
NHLBI NIH HHS R01 HL137808NLM NIH HHS DP2 LM013340
6 · The paper itself

Abstract

Estimation and evaluation of individualized treatment rules have been studied extensively, but real-world treatment resource constraints have received limited attention in existing methods. We investigate a setting in which treatment is intervened upon based on covariates to optimize the mean counterfactual outcome under treatment cost constraints when the treatment cost is random. In a particularly interesting special case, an instrumental variable corresponding to encouragement to treatment is intervened upon with constraints on the proportion receiving treatment. For such settings, we first develop a method to estimate optimal individualized treatment rules. We further construct an asymptotically efficient plug-in estimator of the corresponding average treatment effect relative to a given reference rule.

Identifiers

PMID38323299
PMCPMC10846854

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.