Beyond prediction: Using big data for policy problems

被引：335

作者：

Athey, Susan ^{[1
]}

机构：

[1] Stanford Univ, Grad Sch Business, Stanford, CA 94305 USA

来源：

SCIENCE | 2017年 / 355卷 / 6324期

关键词：

D O I：

10.1126/science.aal4321

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Machine-learning prediction methods have been extremely productive in applications ranging from medicine to allocating fire and health inspectors in cities. However, there are a number of gaps between making a prediction and making a decision, and underlying assumptions need to be understood in order to optimize data-driven decision-making.

引用

页码：483 / 485

页数：3

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