Deep partial least squares for instrumental variable regression |
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Authors: | Maria Nareklishvili Nicholas Polson Vadim Sokolov |
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Institution: | 1. Booth School of Business, University of Chicago, Chicago, Illinois, USA;2. Department of Systems Engineering and Operations Research, George Mason University, Fairfax, Virginia, USA |
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Abstract: | In this paper, we propose deep partial least squares for the estimation of high-dimensional nonlinear instrumental variable regression. As a precursor to a flexible deep neural network architecture, our methodology uses partial least squares for dimension reduction and feature selection from the set of instruments and covariates. A central theoretical result, due to Brillinger (2012) Selected Works of Daving Brillinger. 589-606, shows that the feature selection provided by partial least squares is consistent and the weights are estimated up to a proportionality constant. We illustrate our methodology with synthetic datasets with a sparse and correlated network structure and draw applications to the effect of childbearing on the mother's labor supply based on classic data of Chernozhukov et al. Ann Rev Econ. (2015b):649–688. The results on synthetic data as well as applications show that the deep partial least squares method significantly outperforms other related methods. Finally, we conclude with directions for future research. |
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Keywords: | dimensionality reduction deep learning instrumental variables partial least squares |
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