Model Averaging and Double Machine Learning

54 Pages Posted: 11 Jan 2024

See all articles by Achim Ahrens

Achim Ahrens

ETH Zürich

Christian Hansen

University of Chicago - Booth School of Business - Econometrics and Statistics

Mark Schaffer

Heriot-Watt University; IZA Institute of Labor Economics

Thomas T. Wiemann

University of Chicago - Department of Economics

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

JEL Classification: C21, C26, C52, C55, J01, J08

Suggested Citation

Ahrens, Achim and Hansen, Christian and Schaffer, Mark and Wiemann, Thomas T., Model Averaging and Double Machine Learning. IZA Discussion Paper No. 16714, Available at SSRN: https://ssrn.com/abstract=4691169 or http://dx.doi.org/10.2139/ssrn.4691169

Achim Ahrens (Contact Author)

ETH Zürich ( email )

Zürichbergstrasse 18
8092 Zurich, CH-1015
Switzerland

Christian Hansen

University of Chicago - Booth School of Business - Econometrics and Statistics ( email )

Chicago, IL 60637
United States
773-834-1702 (Phone)

Mark Schaffer

Heriot-Watt University ( email )

Riccarton
Edinburgh EH14 4AS, Scotland EH14 1AS
United Kingdom

IZA Institute of Labor Economics

P.O. Box 7240
Bonn, D-53072
Germany

Thomas T. Wiemann

University of Chicago - Department of Economics ( email )

1101 East 58th Street
Chicago, IL 60637
United States

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