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The Future of Causal Inference
被引:7
|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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