Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects-Reference-Cited by-同舟云学术

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Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects

Published:2024-09-03 Issue: Volume: Page:1-25
ISSN:0162-1459
Container-title:Journal of the American Statistical Association
language:en
Short-container-title:Journal of the American Statistical Association

Author:

Yadlowsky Steve¹^ORCID,Fleming Scott²,Shah Nigam³,Brunskill Emma⁴,Wager Stefan⁵

Affiliation:

1. Google DeepMind

2. Department of Biomedical Data Science, Stanford University

3. Center for Biomedical Informatics Research, Stanford University

4. Department of Computer Science, Stanford University

5. Graduate School of Business, Stanford University

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/01621459.2024.2393466

Reference63 articles.

1. L. M. Artigao-Rodenas J. A. Carbayo-Herencia J. A. Divison-Garrote V. F. Gil-Guillen J. Massó-Orozco M. Simarro-Rueda F. Molina-Escribano C. Sanchis L. Carrión-Valero E. Lopez de Coca et al. Framingham risk score for prediction of cardiovascular diseases: a population-based study from southern Europe. PloS One 8(9):e73529 2013.

2. E. Ascarza. Retention futility: Targeting high-risk customers might be ineffective. Journal of Marketing Research 55(1):80–98 2018.

3. S. Athey and S. Wager. Policy learning with observational data. Econometrica 89(1):133–161 2021.

4. S. Athey J. Tibshirani and S. Wager. Generalized Random Forests. Annals of Statistics 47(2):1148–1178 2019.

5. S. Basu J. B. Sussman and R. A. Hayward. Detecting heterogeneous treatment effects to guide personalized blood pressure treatment: a modeling study of randomized clinical trials. Annals of Internal Medicine 166(5):354–360 2017.

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