Doubly Robust Inference in Causal Latent Factor Models

Author:

Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah

Keyword:

Economics, Econometrics, Econometrics (econ.EM), Machine Learning (cs.LG), Methodology (stat.ME), Machine Learning (stat.ML)

journal:

date:

2024-02-18 00:00:00

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 relevance of the formal properties of the estimators analyzed in this article.

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