Doubly Robust Inference in Causal Latent Factor Models
Econometrics
2024-10-30 v3 Machine Learning
Methodology
Machine Learning
Abstract
This article introduces a new estimator of 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 relevance of the formal properties of the estimators analyzed in this article.
Keywords
Cite
@article{arxiv.2402.11652,
title = {Doubly Robust Inference in Causal Latent Factor Models},
author = {Alberto Abadie and Anish Agarwal and Raaz Dwivedi and Abhin Shah},
journal= {arXiv preprint arXiv:2402.11652},
year = {2024}
}