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摘要
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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.
Keyword:
INSTRUMENTAL VARIABLE ESTIMATION
WEAK INSTRUMENTS
PROPENSITY SCORE
LEARNING-METHODS
SCHOOL QUALITY
LINEAR-MODELS
ESTIMATORS
REGRESSION
INFERENCE
SELECTION
期刊
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5
论文数:
1.4K
被引数:
6.1K
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APPROXIMATE DISTRIBUTIONS OF K-CLASS ESTIMATORS WHEN THE DEGREE OF OVERIDENTIFIABILITY IS LARGE COMPARED WITH THE SAMPLE-SIZE.png)
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收藏Post-Selection and Post-Regularization Inference in Linear Models with Many Controls and Instruments具有许多控件和工具的线性模型中的后选择和后正则化推断AMERICAN ECONOMIC REVIEW
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AMERICAN ECONOMIC REVIEWIF11.6
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