论文标题
仪器可变回归的深度最小二乘
Deep Partial Least Squares for Instrumental Variable Regression
论文作者
论文摘要
在本文中，我们提出了深层最小二乘，以估计高维非线性仪器变量回归。作为灵活的深神经网络体系结构的先驱，我们的方法论使用部分最小二乘来缩小尺寸，并从一组仪器和协变量中选择特征。由于Brillinger（2012），一个中心的理论结果表明，部分最小二乘提供的特征选择是一致的，并且权重估计达到比例常数。我们使用具有稀疏且相关的网络结构的合成数据集说明了我们的方法论，并根据Angrist and Evans的经典数据（1996）将生育对母亲劳动供应的影响吸引了应用程序。合成数据以及应用程序的结果表明，深度最小二乘法可以显着优于其他相关方法。最后，我们以未来研究的指示得出结论。
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) 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 Angrist and Evans (1996). 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.
