ddml: Double/debiased machine learning in Stata

Achim Ahrens, Christian Hansen, Mark Schaffer () and Thomas Wiemann ()
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Thomas Wiemann: University of Chicago

Stata Journal, 2024, vol. 24, issue 1, 3-45

Abstract: 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.

Keywords: ddml; causal inference; machine learning; double/debiased machine learning; doubly robust estimation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (3)

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http://hdl.handle.net/10.1177/1536867X241233641

Related works:
Working Paper: ddml: Double/debiased machine learning in Stata (2024)
Working Paper: ddml: Double/Debiased Machine Learning in Stata (2023)
Working Paper: ddml: Double/debiased machine learning in Stata (2022)
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DOI: 10.1177/1536867X241233641

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