Doubly Robust Inference in Causal Latent Factor Models

Alberto Abadie; Anish Agarwal; Raaz Dwivedi; Abhin Shah

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arXiv · 2402.11652

Doubly Robust Inference in Causal Latent Factor Models

Alberto Abadie·Anish Agarwal·Raaz Dwivedi·Abhin Shah

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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.

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econ.EM cs.LG stat.ME stat.ML

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Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah. 2024-02-18. Doubly Robust Inference in Causal Latent Factor Models. https://arxiv.org/abs/2402.11652

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