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.

翻译：本文探讨将双重/去偏机器学习（DDML）与模型平均方法——堆叠法相结合，以估计结构参数。除传统堆叠法外，我们提出了两种适用于DDML的改进方案：短堆叠法利用DDML的交叉拟合步骤显著降低计算负担，而合并堆叠法则强制要求交叉拟合折痕间采用统一的堆叠权重。通过校准模拟研究以及两个实际应用（分别估计学术引用和工资中的性别差异），我们证明基于堆叠法的DDML相较于依赖单一预设学习器的常规方法，对部分未知函数形式具有更强的鲁棒性。我们提供了实现该方法的Stata与R语言软件包。