post-double-lasso
Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso
Hué, Sullivan, Laurent, Sébastien, Aiounou, Ulrich, Flachaire, Emmanuel
Post-Double-Lasso is becoming the most popular method for estimating linear regression models with many covariates when the purpose is to obtain an accurate estimate of a parameter of interest, such as an average treatment effect. However, this method can suffer from substantial omitted variable bias in finite sample. We propose a new method called Post-Double-Autometrics, which is based on Autometrics, and show that this method outperforms Post-Double-Lasso.
2511.21257
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- Europe > United Kingdom > England > Oxfordshire > Oxford (0.04)
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