Model Averaging and Double Machine Learning
模型平均与双重机器学习
ABSTRACT This paper discusses pairing double/debiased machine learning (DDML) with stacking , a model averaging method for combining multiple candidate learners, to estimate structural parameters. In addition to conventional stacking, we consider two stacking variants available for DDML: Short‐stacking exploits the cross‐fitting step of DDML to substantially reduce the computational burden, and pooled stacking enforces common stacking weights over cross‐fitting folds. Using…