ddml: Double/debiased machine learning in Stata
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866913187320823808 |
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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 | We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_09397 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | ddml: Double/debiased machine learning in Stata Ahrens, Achim Hansen, Christian B. Schaffer, Mark E. Wiemann, Thomas Econometrics Machine Learning We introduce the package ddml for Double/Debiased Machine Learning (DDML) in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms and/or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using DDML in combination with stacking estimation which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation. |
| title | ddml: Double/debiased machine learning in Stata |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2301.09397 |