The post-double selection Lasso estimator has become a popular way of selecting control variables when analyzing randomized experiments. This is done to try to improve precision, and reduce bias from attrition or chance imbalances. This paper re-estimates 780 treatment effects from published papers to examine how much difference this approach makes in practice. PDS Lasso is found to reduce standard errors by less than one percent compared to standard...
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INFORMATION
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2024/09/26
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Document de travail de recherche sur les politiques
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WPS10931
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1
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2024/09/26
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Disclosed
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Using Post-Double Selection Lasso in Field Experiments
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