Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders. (April 2023)

• Record Type: Journal Article
• Authors: Yi, Grace Y.; Chen, Li-Pang
• Is Part Of: Statistical methods in medical research. Volume 32:Number 4(2023)
• Page Start: 691
• Page End: 711
• Publication Date: 2023-04
• Subjects: Causal inference -- inverse probability weight -- measurement error -- propensity score -- simulation–extrapolation -- variable selection; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/09622802221146308
• Languages: English
• ISSNs: 0962-2802

Abstract

In the framework of causal inference, the inverse probability weighting estimation method and its variants have been commonly employed to estimate the average treatment effect. Such methods, however, are challenged by the presence of irrelevant pre-treatment variables and measurement error. Ignoring these features and naively applying the usual inverse probability weighting estimation procedures may typically yield biased inference results. In this article, we develop an inference method for estimating the average treatment effect with those features taken into account. We establish theoretical properties for the resulting estimator and carry out numerical studies to assess the finite sample performance of the proposed estimator.