ddml: Double/debiased machine learning in Stata
Achim Ahrens, Christian Hansen, Mark E. Schaffer, Thomas Wiemann
2024The Stata Journal Promoting communications on statistics and Stata49 citationsDOIOpen Access PDF
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 effects 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 learning in combination with stacking estimation, which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation.
Topics & Concepts
EstimatorComputer scienceMachine learningArtificial intelligenceMonte Carlo methodEconometricsStatisticsMathematicsAdvanced Causal Inference TechniquesMonetary Policy and Economic ImpactStatistical Methods and Inference