This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches 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.

翻译：本文探讨了将双重/去偏机器学习（DDML）与堆叠法（一种结合多个候选学习器的模型平均方法）配对使用，以估计结构参数。我们引入了两种新的DDML堆叠方法：短堆叠利用DDML的交叉拟合步骤显著降低计算负担，而池化堆叠则在交叉拟合折上强制使用共同的堆叠权重。通过校准模拟研究以及两个关于引文和工资中的性别差距的应用案例，我们证明，与基于单一预选学习器的常用替代方法相比，采用堆叠法的DDML对部分未知函数形式具有更强的鲁棒性。我们提供了实现我们所提方法的Stata和R软件。