ddml: Double/debiased machine learning in Stata-Reference-Cited by-同舟云学术

ddml: Double/debiased machine learning in Stata

Published:2024-03 Issue:1 Volume:24 Page:3-45
ISSN:1536-867X
Container-title:The Stata Journal: Promoting communications on statistics and Stata
language:en
Short-container-title:The Stata Journal: Promoting communications on statistics and Stata

Author:

Ahrens Achim¹,Hansen Christian B.²,Schaffer Mark E.³,Wiemann Thomas²

Affiliation:

1. ETH Zürich, Zürich, Switzerland,

2. University of Chicago, Chicago, IL,

3. Heriot-Watt University, Edinburgh, U.K.,

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.

Publisher

SAGE Publications

Link

https://journals.sagepub.com/doi/pdf/10.1177/1536867X241233641

Reference43 articles.

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2. lassopack: Model selection and prediction with regularized regression in Stata

3. pystacked: Stacking generalization and machine learning in Stata

4. Ahrens A., Hansen C. B., Schaffer M. E., Wiemann T. 2024. Model averaging and double machine learning. ArXiv:2401.01645 [econ.EM]. https://doi.org/10.48550/arXiv.2401.01645.

5. The Interpretation of Instrumental Variables Estimators in Simultaneous Equations Models with an Application to the Demand for Fish

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