• Media type: E-Book
  • Title: ddml : Double/Debiased Machine Learning in Stata
  • Contributor: Ahrens, Achim [VerfasserIn]; Hansen, Christian Bailey [VerfasserIn]; Schaffer, Mark E. [VerfasserIn]; Wiemann, Thomas [VerfasserIn]
  • imprint: Bonn, Germany: IZA - Institute of Labor Economics, February 2023
  • Published in: Forschungsinstitut zur Zukunft der Arbeit: Discussion paper series ; 15963
  • Extent: 1 Online-Ressource (circa 52 Seiten)
  • Language: English
  • Identifier:
  • Keywords: st0001 ; causal inference ; machine learning ; doubly-robust estimation ; Graue Literatur
  • Origination:
  • Footnote:
  • Description: 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.
  • Access State: Open Access