Causal inference in economics and marketing

被引：131

作者：

Varian, Hal R. ^{[1
]}

机构：

[1] Google Inc, Econ Team, Mountain View, CA 94043 USA

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2016年 / 113卷 / 27期

关键词：

causal inference; economics; machine learning; marketing;

D O I：

10.1073/pnas.1510479113

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：

This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

引用

页码：7310 / 7315

页数：6

共 24 条

[1] Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California's Tobacco Control Program [J].