Model Averaging and Double Machine Learning
研究将双/去偏机器学习与堆叠法（一种模型平均方法）结合，以估计结构参数，并通过模拟和实证表明该方法比单一预选学习器更稳健。
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 calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre‐selected learners. We provide Stata and R software implementing our proposals.