{"ID":2836346,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.21257","arxiv_id":"2511.21257","title":"Estimation in high-dimensional linear regression: Post-Double-Autometrics as an alternative to Post-Double-Lasso","abstract":"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. Its use in a standard application of economic growth sheds new light on the hypothesis of convergence from poor to rich economies.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2511.21257","url_pdf":"https://arxiv.org/pdf/2511.21257v1","authors":"[\"Sullivan Hué\",\"Sébastien Laurent\",\"Ulrich Aiounou\",\"Emmanuel Flachaire\"]","published":"2025-11-26T10:39:25Z","proceeding":"econ.EM","tasks":"[\"econ.EM\",\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}