Introduction to the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery

被引：5

作者：

Kosorok, Michael R. ^{[1
,2
]}

Laber, Eric B. ^{[3
]}

Small, Dylan S. ^{[4
]}

Zeng, Donglin ^{[1
]}

机构：

[1] Univ N Carolina, Dept Biostat, Chapel Hill, NC 27599 USA

[2] Univ N Carolina, Dept Stat & Operat Res, Chapel Hill, NC 27515 USA

[3] North Carolina State Univ, Dept Stat, Raleigh, NC 27695 USA

[4] Univ Penn, Dept Stat, Philadelphia, PA 19104 USA

来源：

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | 2021年 / 116卷 / 533期

关键词：

Actionable artificial intelligence; Causal inference; Machine learning;

D O I：

10.1080/01621459.2020.1863224

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We introduce the Theory and Methods Special Issue on Precision Medicine and Individualized Policy Discovery. The issue consists of four discussion papers, grouped into two pairs, and sixteen regular research papers that cover many important lines of research on data-driven decision making. We hope that the many provocative and original ideas presented herein will inspire further work and development in precision medicine and personalization.

引用

页码：159 / 161

页数：3

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