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Journal of the American Statistical Association Volume 116, 2021 - Issue 534
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Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery, Part II

More Efficient Policy Learning via Optimal Retargeting

Nathan KallusSchool of Operations Research and Information Engineering and Cornell Tech, Cornell University, New York, NYCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-2757-1570View further author information
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Pages 646-658 | Received 19 Jun 2019, Accepted 23 Jun 2020, Published online: 03 Aug 2020

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Read on this site (5)

Stijn Vansteelandt, Oliver Dukes, Kelly Van Lancker & Torben Martinussen. (2024) Assumption-Lean Cox Regression. Journal of the American Statistical Association 119:545, pages 475-484.
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Weibin Mo, Zhengling Qi & Yufeng Liu. (2021) Rejoinder: Learning Optimal Distributionally Robust Individualized Treatment Rules. Journal of the American Statistical Association 116:534, pages 699-707.
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Nathan Kallus. (2021) Rejoinder: New Objectives for Policy Learning. Journal of the American Statistical Association 116:534, pages 694-698.
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Sijia Li, Xiudi Li & Alex Luedtke. (2021) Discussion of Kallus (2020) and Mo, Qi, and Liu (2020): New Objectives for Policy Learning. Journal of the American Statistical Association 116:534, pages 680-689.
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Muxuan Liang & Ying-Qi Zhao. (2021) Discussion of Kallus (2020) and Mo et al. (2020). Journal of the American Statistical Association 116:534, pages 690-693.
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Articles from other publishers (3)

Rui Chen, Jared D Huling, Guanhua Chen & Menggang Yu. (2024) Robust sample weighting to facilitate individualized treatment rule learning for a target population. Biometrika 111:1, pages 309-329.
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Nathan Kallus & Michele Santacatterina. (2022) Optimal weighting for estimating generalized average treatment effects. Journal of Causal Inference 10:1, pages 123-140.
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Uri Shalit. (2022) Commentary on “Causal Decision Making and Causal Effect Estimation Are Not the Same…and Why It Matters”. INFORMS Journal on Data Science 1:1, pages 19-20.
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