ddml: Double/Debiased Machine Learning in Stata

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

No 15963, IZA Discussion Papers from IZA Network @ LISER

Abstract: 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.

Keywords: doubly-robust estimation; machine learning; causal inference; st0001 (search for similar items in EconPapers)
JEL-codes: C14 C21 C87 (search for similar items in EconPapers)
Pages: 52 pages
Date: 2023-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dcm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published - published in: Stata Journal, 2024, 24 (1), 3-45.

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Related works:
Journal Article: ddml: Double/debiased machine learning in Stata (2024)
Working Paper: ddml: Double/debiased machine learning in Stata (2024)
Working Paper: ddml: Double/debiased machine learning in Stata (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp15963

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