Doubly Robust Inference in Causal Latent Factor Models

Alberto Abadie, Anish Agarwal, Raaz Dwivedi and Abhin Shah

Papers from arXiv.org

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.

Date: 2024-02, Revised 2024-10
New Economics Papers: this item is included in nep-ecm
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