Model Averaging and Double Machine Learning

Achim Ahrens, Christian B. Hansen, Mark Schaffer () and Thomas Wiemann

Papers from arXiv.org

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.

Date: 2024-01, Revised 2024-09
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ecm
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Working Paper: Model Averaging and Double Machine Learning (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2401.01645

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