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Erscheinungsjahr: 
2024
Schriftenreihe/Nr.: 
IZA Discussion Papers No. 16714
Verlag: 
Institute of Labor Economics (IZA), Bonn
Zusammenfassung: 
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.
Schlagwörter: 
causal inference
partially linear model
high-dimensional models
super learners
nonparametric estimation
JEL: 
C21
C26
C52
C55
J01
J08
Dokumentart: 
Working Paper

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Datei
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