过度拟合
工具变量
Lasso(编程语言)
计量经济学
劳动经济学
特征选择
经济
虚假关系
回归
选择(遗传算法)
选择偏差
弹性网正则化
工资
推论
计算机科学
统计
人工智能
数学
万维网
人工神经网络
作者
Joshua D. Angrist,Brigham R. Frandsen
摘要
The utility of machine learning (ML) for regression-based causal inference is illustrated by using lasso to select control variables for estimates of college characteristics’ wage effects. Post-double-selection lasso offers a path to data-driven sensitivity analysis. ML also seems useful for an instrumental variables (IV) first stage, since two-stage least squares (2SLS) bias reflects overfitting. While ML-based instrument selection can improve on 2SLS, split-sample IV and limited information maximum likelihood do better. Finally, we use ML to choose IV controls. Here, ML creates artificial exclusion restrictions, generating spurious findings. On balance, ML seems ill-suited to IV applications in labor economics.
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