Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/282841 
Year of Publication: 
2024
Series/Report no.: 
IZA Discussion Papers No. 16714
Publisher: 
Institute of Labor Economics (IZA), Bonn
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.
Subjects: 
causal inference
partially linear model
high-dimensional models
super learners
nonparametric estimation
JEL: 
C21
C26
C52
C55
J01
J08
Document Type: 
Working Paper

Files in This Item:
File
Size
3.38 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.