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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 LP, 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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    Keywords

    ddml; causal inference; machine learning; double/debiased machine learning; doubly robust estimation;
    All these keywords.

    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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