On the Asymptotic Inadmissibility of Double Machine Learning Estimators Under Structure-Agnostic Models
The episode discusses a paper analyzing Double Machine Learning (DML) estimators, concluding that DML is not always optimal for all data functions. Hosts explain that while DML is powerful, certain parameters require advanced second-order methods, such as HOIF estimators, to correct systematic bias and achieve higher precision.