Large Dimensional Latent Factor Modeling with Missing Observations and Applications to Causal Inference

2019/10/18 by Ruoxuan Xiong, Markus Pelger, Xiong, Ruoxuan +1 · 3 citations
Mathematics · #Advanced Causal Inference Techniques #Econometrics (econ.EM) #FOS: Economics and business

paper · pdf · doi:10.48550/arxiv.1910.08273

openalex publication_date 2019/10/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time under general adoption patterns where the units can be affected by unobserved factors.

Cited by

• Counterfactual Forecasting For Panel Data
• Causal Representation Learning from Multimodal Clinical Records under Non-Random Modality Missingness
• Conformal Link Prediction with False Discovery Rate Control

Related

• Estimation of Semiparametric Factor Models with Missing Data
• Factor-Based Imputation of Missing Values and Covariances in Panel Data of Large Dimensions
• Statistical Inference for High-dimensional Matrix-variate Factor Models with Missing Observations
• Doubly Robust Inference in Causal Latent Factor Models
• Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities