The Future of Causal Inference

被引:6
|
作者
Mitra, Nandita [1 ]
Roy, Jason [2 ]
Small, Dylan [3 ]
机构
[1] Univ Penn, Dept Biostat Epidemiol & Informat, 423 Guardian Dr, Philadelphia, PA 19104 USA
[2] Rutgers Sch Publ Hlth, Dept Biostat & Epidemiol, Piscataway, NJ USA
[3] Univ Penn, Dept Stat, Philadelphia, PA 19104 USA
基金
美国国家卫生研究院;
关键词
algorithms; causal discovery; causal machine learning; distributed learning; high-dimensional data; interference; transportability;
D O I
10.1093/aje/kwac108
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
The past several decades have seen exponential growth in causal inference approaches and their applications. In this commentary, we provide our top-10 list of emerging and exciting areas of research in causal inference. These include methods for high-dimensional data and precision medicine, causal machine learning, causal discovery, and others. These methods are not meant to be an exhaustive list; instead, we hope that this list will serve as a springboard for stimulating the development of new research.
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页码:1671 / 1676
页数:6
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