Discussion paper
DP19505 Using Post-Double Selection Lasso in Field Experiments
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. We re-estimate 780 treatment effects from published papers to examine how much difference this approach makes in practice. We find it reduces standard errors by less than one percent compared to standard Ancova on average and does not select variables to model treatment in over half the cases. We discuss and provide evidence on the key practical decisions researchers face in using this method.
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