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In addition to conventional stacking, we consider two stacking variants available for DDML: <jats:italic>Short\u2010stacking<\/jats:italic> exploits the cross\u2010fitting step of DDML to substantially reduce the computational burden, and <jats:italic>pooled stacking<\/jats:italic> enforces common stacking weights over cross\u2010fitting 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\u2010selected learners. 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