ddml: Double/debiased machine learning in Stata
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 67 references that are in the map. First posted on arXiv 23 Jan 2023.
Builds on
7 of 67 references.
References · 7
- 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 597
- 2017Nonparametric regression using deep neural networks with ReLU activation functioncited by 431
- 2016Generalized Random Forestscited by 309
- 2013Program Evaluation and Causal Inference with High-Dimensional Datacited by 29
- 2016High-Dimensional L2Boosting: Rate of Convergencecited by 10
- 2016High-Dimensional Metrics in Rcited by 2
- 2022pystacked: Stacking generalization and machine learning in Statacited by 1
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Citing papers per year: 2025: 1.
1 paper.