Doubly Robust Inference in Causal Latent Factor Models

arXiv:2402.11652v1 Announce Type: cross
Abstract: This article introduces a new framework for estimating average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. The proposed estimator is doubly robust, combining outcome imputation, inverse probability weighting, and a novel cross-fitting procedure for matrix completion. We derive finite-sample and asymptotic guarantees, and show that the error of the new estimator converges to a mean-zero Gaussian distribution at a parametric rate. Simulation results demonstrate the practical …

abstract article arxiv confounding cs.lg data econ.em effects environments framework imputation inference matrix modern novel numbers probability robust stat.me stat.ml treatment type units