  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.
