High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.

Wang T, Pate V, Wyss R, Buse JB, Kosorok MR, Stürmer T

DOI: 10.1093/aje/kwae322
Published: 2024 Sep 5
Container: American journal of epidemiology
Publisher: Not recorded
Open access: yes

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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

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Cite this work

BibTeX

@article{allodium:10.1093/aje/kwae322,
  title = {High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.},
  author = {Wang T and Pate V and Wyss R and Buse JB and Kosorok MR and Stürmer T},
  year = {2024},
  journal = {American journal of epidemiology},
  doi = {10.1093/aje/kwae322},
  url = {https://doi.org/10.1093/aje/kwae322}
}

RIS

TY  - JOUR
TI  - High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.
AU  - Wang T
AU  - Pate V
AU  - Wyss R
AU  - Buse JB
AU  - Kosorok MR
AU  - Stürmer T
PY  - 2024
JO  - American journal of epidemiology
DO  - 10.1093/aje/kwae322
UR  - https://doi.org/10.1093/aje/kwae322
ER  -

APA

T, W., V, P., R, W., JB, B., MR, K., & T, S. (2024). High-dimensional Iterative Causal Forest (hdiCF) for Subgroup Identification Using Health Care Claims Data.. American journal of epidemiology. https://doi.org/10.1093/aje/kwae322

Source records

pubmed · retrieved 2026-09-27T00:08:33.017Z
europe-pmc · retrieved 2026-09-27T00:08:33.023Z