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ddml: Double/debiased machine learning in Stata

Author

Listed:
  • Christian B. Hansen

    (University of Chicago)

  • Mark E. Schaffer

    (Heriot-Watt University)

  • Thomas Wiemann

    (University of Chicago)

  • Achim Ahrens

    (ETH Zürich)

Abstract

We introduce the Stata package ddml, which implements double/debiased machine learning (DDML) for causal inference aided by supervised machine learning. Five different models are supported, allowing for multiple treatment variables in the presence of high-dimensional controls and instrumental variables. ddml is compatible with many existing supervised machine learning programs in Stata.

Suggested Citation

  • Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann & Achim Ahrens, 2022. "ddml: Double/debiased machine learning in Stata," Swiss Stata Conference 2022 02, Stata Users Group.
    • Handle: RePEc:boc:csug22:02
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    File URL: http://repec.org/csug2022/Ahrens-Bern2022-ddml.pdf
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    2. Lee, Chenyang & Ogata, Seiichi, 2025. "Every coin has two sides: Dual effects of energy transition on regional sustainable development—A quasi-natural experiment of the New Energy Demonstration City Pilot Policy," Applied Energy, Elsevier, vol. 390(C).
    3. Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2025. "Model Averaging and Double Machine Learning," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 40(3), pages 249-269, April.
    4. Yuchen Lu & Jiakun Zhuang & Jun Chen & Chenlu Yang & Mei Kong, 2025. "The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning," Land, MDPI, vol. 14(1), pages 1-30, January.
    5. Da Gao & Qingshuo Wang & Qingjiang Han, 2025. "How Does Critical Peak Pricing Boost Urban Green Total Factor Energy Efficiency? Evidence from a Double Machine Learning Model," Energies, MDPI, vol. 18(18), pages 1-21, September.
    6. Ciżkowicz, Piotr & Ledóchowski, Michał & Rzońca, Andrzej, 2025. "Fiscal policy and government bond yields: New evidence from the EU," Economic Modelling, Elsevier, vol. 147(C).
    7. Bonaccolto-Töpfer, Marina & Satlukal, Sascha, 2024. "Gender differences in reservation wages: New evidence for Germany," Labour Economics, Elsevier, vol. 91(C).
    8. Ding, Yijiu & Li, Bo & Lan, Dahai & Yu, Chunrong & Zhang, Xueqing, 2025. "Research on wage distortion in R&D and innovation activities —— Evidence from China's listed manufacturing enterprises," International Review of Economics & Finance, Elsevier, vol. 102(C).
    9. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    10. Girma, Sourafel & Paton, David, 2024. "Using double-debiased machine learning to estimate the impact of Covid-19 vaccination on mortality and staff absences in elderly care homes," European Economic Review, Elsevier, vol. 170(C).

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

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