Arbeitspapier

ddml: Double/Debiased Machine Learning in Stata

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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.

Sprache
Englisch

Erschienen in
Series: IZA Discussion Papers ; No. 15963

Klassifikation
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Econometric Software
Thema
st0001
causal inference
machine learning
doubly-robust estimation

Ereignis
Geistige Schöpfung
(wer)
Ahrens, Achim
Hansen, Christian B.
Schaffer, Mark E
Wiemann, Thomas
Ereignis
Veröffentlichung
(wer)
Institute of Labor Economics (IZA)
(wo)
Bonn
(wann)
2023

Handle
Letzte Aktualisierung
10.03.2025, 11:41 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Original beim Datenpartner anzeigen

Objekttyp

  • Arbeitspapier

Beteiligte

  • Ahrens, Achim
  • Hansen, Christian B.
  • Schaffer, Mark E
  • Wiemann, Thomas
  • Institute of Labor Economics (IZA)

Entstanden

  • 2023

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