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ddml: Double/debiased machine learning in Stata

Author

Listed:
  • Achim Ahrens

    (ETH Zürich)

  • Christian B. Hansen

    (University of Chicago)

  • Mark E. Schaffer

    (Heriot-Watt University)

  • Thomas Wiemann

    (University of Chicago)

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.

Suggested Citation

  • Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2024. "ddml: Double/debiased machine learning in Stata," Stata Journal, StataCorp LLC, vol. 24(1), pages 3-45, March.
    • Handle: RePEc:tsj:stataj:v:24:y:2024:i:1:p:3-45
    DOI: 10.1177/1536867X241233641
    Note: to access software from within Stata, net describe http://www.stata-journal.com/software/sj24-1/st0738/
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    Cited by:

    1. Piotr Ciżkowicz & Andrzej Rzońca & Wiktor Wojciechowski, 2025. "Does fiscal devaluation always work? A sectoral and business cycle perspective," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 52(4), pages 777-810, November.
    2. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    3. Zhang, Shengwu & Han, Liyan, 2026. "Low-altitude infrastructure and economic growth: Evidence from general aviation airports," Transport Policy, Elsevier, vol. 175(C).
    4. Ding, Yijiu & Li, Bo & Ma, Shenglin & Yu, Chunrong & Zhao, Xiaoyi, 2025. "Digital transformation and wage distortion in R&D and innovation activities - Causal inference based on double machine learning," International Review of Financial Analysis, Elsevier, vol. 106(C).
    5. 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).
    6. Lüthi, Samuel, 2025. "Classroom versus workbench: The impact of firm-based learning on labour market and educational outcomes," Economics of Education Review, Elsevier, vol. 109(C).
    7. 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).
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. Ciżkowicz, Piotr & Ledóchowski, Michał & Rzońca, Andrzej, 2025. "Fiscal policy and government bond yields: New evidence from the EU," Economic Modelling, Elsevier, vol. 147(C).
    13. Bonaccolto-Töpfer, Marina & Satlukal, Sascha, 2024. "Gender differences in reservation wages: New evidence for Germany," Labour Economics, Elsevier, vol. 91(C).
    14. Squarcina, Margherita, 2025. "Exploring the Effects of Price Stabilization on Coffee Income Among Farmers in Rwanda: Does Certification Matter?," Sustainable Food Systems Discussion Papers 378988, Georg-August-Universitaet Goettingen, Department of Agricultural Economics and Rural Development.

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    Keywords

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    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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