Model Averaging and Double Machine Learning

• URL: http://arxiv.org/abs/2401.01645v2
• Date: Wed, 25 Sep 2024 20:56:11 GMT
• Title: Model Averaging and Double Machine Learning
• Authors: Achim Ahrens, Christian B. Hansen, Mark E. Schaffer, Thomas Wiemann,
• Abstract summary: We show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches. We provide Stata and R software implementing our proposals.
• Score: 2.6436521007616114
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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