ddml: Double/debiased machine learning in Stata

• Autores: Achim Ahrens, Christian B. Hansen, Mark E. Schaffer, Thomas Wiemann
• Localización: The Stata journal, ISSN 1536-867X, Vol. 24, Nº. 1, 2024, págs. 3-45
• Idioma: inglés
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• Resumen

  • In this article, we introduce a package, ddml, for double/debiased machine learning in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal ef- fects of endogenous variables in settings with unknown functional forms or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using double/debiased machine learn- ing in combination with stacking estimation, which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.