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. 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 Ancova on average and does not select variables to model treatment in over half the cases. The authors discuss and provide evidence on the key practical decisions researchers face in using this method.