Model Averaging and Double Machine Learning

Achim Ahrens, Christian B. Hansen (), Mark Schaffer () and Thomas Wiemann ()
Additional contact information
Christian B. Hansen: University of Chicago
Thomas Wiemann: University of Chicago

No 16714, IZA Discussion Papers from IZA Network @ LISER

Abstract: 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.

Keywords: super learners; causal inference; partially linear model; high-dimensional models; nonparametric estimation (search for similar items in EconPapers)
JEL-codes: C21 C26 C52 C55 J01 J08 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2024-01
New Economics Papers: this item is included in nep-big and nep-lab
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published - published in: Journal of Applied Economics , 2025, 40 (3), 249-269

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https://docs.iza.org/dp16714.pdf (application/pdf)

Related works:
Journal Article: Model Averaging and Double Machine Learning (2025)
Working Paper: Model Averaging and Double Machine Learning (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp16714

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