Model Averaging and Double Machine Learning

Achim Ahrens*, Christian B. Hansen, Mark Edwin Schaffer, Thomas Wiemann

*Corresponding author for this work

Research output: Working paper


This paper discusses pairing double/debiased machine learning (DDML) with stacking, a model averaging method for combining multiple candidate learners, to estimate structural parameters. We introduce two new stacking approaches for DDML: short-stacking exploits the cross-fitting step of DDML to substantially reduce the computational burden and pooled stacking enforces common stacking weights over cross-fitting folds. Using calibrated simulation studies and two applications estimating gender gaps in citations and wages, we show that DDML with stacking is more robust to partially unknown functional forms than common alternative approaches based on single pre-selected learners. We provide Stata and R software implementing our proposals.
Original languageEnglish
Place of PublicationBonn, Germany
PublisherIZA Institute of Labor Economics
Publication statusPublished - 10 Jan 2024

Publication series

NameDiscussion Paper Series
ISSN (Print)2365-9793


  • causal inference
  • partially linear model
  • high-dimensional models
  • super learners
  • nonparametric estimation
  • gender gap

ASJC Scopus subject areas

  • Economics and Econometrics


Dive into the research topics of 'Model Averaging and Double Machine Learning'. Together they form a unique fingerprint.

Cite this