Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data.

Pryce M, Diaz-Ordaz K, Keogh RH, Vansteelandt S

DOI: 10.1093/biomtc/ujaf098
Published: 2025 Jul 3
Container: Biometrics
Publisher: Not recorded
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1093/biomtc/ujaf098,
  title = {Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data.},
  author = {Pryce M and Diaz-Ordaz K and Keogh RH and Vansteelandt S},
  year = {2025},
  journal = {Biometrics},
  doi = {10.1093/biomtc/ujaf098},
  url = {https://doi.org/10.1093/biomtc/ujaf098}
}

RIS

TY  - JOUR
TI  - Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data.
AU  - Pryce M
AU  - Diaz-Ordaz K
AU  - Keogh RH
AU  - Vansteelandt S
PY  - 2025
JO  - Biometrics
DO  - 10.1093/biomtc/ujaf098
UR  - https://doi.org/10.1093/biomtc/ujaf098
ER  -

APA

M, P., K, D., RH, K., & S, V. (2025). Causal machine learning for heterogeneous treatment effects in the presence of missing outcome data.. Biometrics. https://doi.org/10.1093/biomtc/ujaf098

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pubmed · retrieved 2026-09-25T23:31:35.568Z