6 papers
Debiased Conditional Independence Tests for Causal Effects
Susan Athey, Maria Nareklishvili, Jann Spiess
We propose robust tests for causal effects on potentially high-dimensional outcomes where there may be selection into treatment. These tests measure conditional independence of out…
Generative Quantile Bayesian Prediction
Maria Nareklishvili, Nick Polson, Vadim Sokolov
Prediction is a central task of machine learning. Our goal is to solve large scale prediction problems using Generative Quantile Bayesian Prediction (GQBP).By directly learning pre…
Generative Learner for Distributional Causal Effects
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
We propose a generative learner for estimating conditional average treatment effects and characterizing the full distribution of these effects. The learner takes the form of a mult…
Generative Causal Inference
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
Generative Bayesian Computation (GBC) methods are developed for Casual Inference. Generative methods are simulation-based methods that use a large training dataset to represent pos…
Feature Selection for Personalized Policy Analysis
Maria Nareklishvili, Nicholas Polson, Vadim Sokolov
In this paper, we propose Forest-PLS, a feature selection method for analyzing policy effect heterogeneity in a more flexible and comprehensive manner than is typically available w…
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…