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

来源：

AMERICAN JOURNAL OF EPIDEMIOLOGY | 2022年 / 191卷 / 10期

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.

引用

页码：1671 / 1676

页数：6

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