ddml: Double/debiased machine learning in Stata

Achim Ahrens, Christian Hansen, Mark Schaffer () and Thomas Wiemann

Papers from arXiv.org

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.

Date: 2023-01, Revised 2024-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-dcm
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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 (2023)
Working Paper: ddml: Double/debiased machine learning in Stata (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2301.09397

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