Double machine learning and Stata application

Traditional methods for estimating treatment effects generally assume strong functional forms and are only applicable when the covariates are low-dimensional data. However, using machine learning methods directly often leads to "regularization bias". The recently emerging "double/debiased machine learning" provides an effective estimation method without assuming a functional form and is suitable for high-dimensional data. This presentation will introduce the principles of dual machine learning in a simple way and demonstrate the corresponding Stata operations with classic cases.