TY  - RPRT
        AU  - Ahrens, Achim
AU  - Hansen, Christian B.
AU  - Schaffer, Mark E
AU  - Wiemann, Thomas
        TI  - Model Averaging and Double Machine Learning
        PY  - 2024/Jan/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 16714
        UR  - https://www.iza.org/index.php/publications/dp16714
        AB  - 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.
        KW  - super learners
KW  - causal inference
KW  - partially linear model
KW  - high-dimensional models
KW  - nonparametric estimation
        ER  -