Doubly Robust Inference in Causal Latent Factor Models
This addresses causal inference challenges in high-dimensional data for researchers and practitioners, representing an incremental improvement through a novel combination of existing techniques.
The paper tackles the problem of estimating average treatment effects under unobserved confounding in data-rich settings by introducing a doubly robust estimator that combines outcome imputation, inverse probability weighting, and cross-fitting for matrix completion, with results showing error convergence to a mean-zero Gaussian distribution at a parametric rate.
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.