Model Averaging and Double Machine Learning
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916411286224896 |
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| author | Ahrens, Achim Hansen, Christian B. Schaffer, Mark E. Wiemann, Thomas |
| author_facet | Ahrens, Achim Hansen, Christian B. Schaffer, Mark E. Wiemann, Thomas |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01645 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model Averaging and Double Machine Learning Ahrens, Achim Hansen, Christian B. Schaffer, Mark E. Wiemann, Thomas Econometrics Machine Learning 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. |
| title | Model Averaging and Double Machine Learning |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2401.01645 |