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 Institute of Labor Economics (IZA)

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: causal inference; partially linear model; high-dimensional models; super learners; 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
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Citations: View citations in EconPapers (1)

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Working Paper: Model Averaging and Double Machine Learning (2024)
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