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Doubly Robust Inference in Causal Latent Factor Models
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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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Cited by 2 Pith papers
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Causal Inference for Sequential Settings under Interference and Latent Confounding
A sequential maximum-pseudo-likelihood estimator learns a Markov-chain Ising model with low-rank latent confounding from a single observational panel, with provable parameter and generalized-treatment-effect error bou...
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Latent Variable Modeling for Robust Causal Effect Estimation
Latent DML fits a parametric latent variable model to DML residuals and adjusts the outcome residual before the final effect regression, yielding consistent estimates under well-specified unobserved confounding.
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