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Deep Partial Least Squares for Instrumental Variable Regression
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
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 net…
Deep Partial Least Squares for Empirical Asset Pricing
Matthew F. Dixon, Nicholas G. Polson, Kemen Goicoechea
We use deep partial least squares (DPLS) to estimate an asset pricing model for individual stock returns that exploits conditioning information in a flexible and dynamic way while…
Inverse Probability Weighting: from Survey Sampling to Evidence Estimation
Jyotishka Datta, Nicholas Polson
We consider the class of inverse probability weight (IPW) estimators, including the popular Horvitz-Thompson and Hajek estimators used routinely in survey sampling, causal inferenc…