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. In addition to conventional stacking, we consider two stacking variants available 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 language | English |
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Journal | Journal of Applied Econometrics |
Early online date | 19 Jan 2025 |
DOIs | |
Publication status | E-pub ahead of print - 19 Jan 2025 |
Keywords
- causal inference
- partially linear model
- high-dimensional models
- super learners
- nonparametric estimation
ASJC Scopus subject areas
- Economics, Econometrics and Finance(all)