Doubly Robust Inference in Causal Latent Factor Models

2024/02/18 by Alberto Abadie, Abadie, Alberto, Anish Agarwal +5 · 3 citations
Computer Science · #Bayesian Modeling and Causal Inference #Econometrics (econ.EM) #FOS: Computer and information sciences #FOS: Economics and business #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Methodology (stat.ME)

paper · pdf · doi:10.48550/arxiv.2402.11652

openalex publication_date 2024/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• On robust Bayesian causal inference
• Latent Variable Modeling for Robust Causal Effect Estimation
• Differentiable Cyclic Causal Discovery Under Unmeasured Confounders

Related

• A nonparametric super-efficient estimator of the average treatment\n effect
• High-dimensional Inference for Dynamic Treatment Effects
• Doubly Robust Estimation of Causal Effects
• Doubly Robust Proximal Causal Inference under Confounded Outcome-Dependent Sampling
• Double-robust and efficient methods for estimating the causal effects of a binary treatment