Related papers: Doubly Robust Inference in Causal Latent Factor Models

Doubly Robust Inference in Causal Latent Factor Models

URL: http://arxiv.org/abs/2402.11652v2
Date: Mon, 15 Apr 2024 16:39:15 GMT
Title: Doubly Robust Inference in Causal Latent Factor Models
Authors: Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah,
Abstract summary: 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. We derive finite-sample weighting and guarantees, and show that the error of the new estimator converges to a mean-zero Gaussian distribution at a parametric rate.
Score: 12.116813197164047
License: http://creativecommons.org/licenses/by/4.0/
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 practical relevance of the formal properties of the estimators analyzed in this article.

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