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Abstract Details

Activity Number: 457
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract - #305207
Title: Inverse Probability Weighting with Error Prone Covariates
Author(s): Claude Setodji*+ and Daniel F. McCaffrey and J.R. Lockwood
Companies: RAND Corporation and RAND Corporation and RAND Corporation
Address: 4570 5th Avenue, Pittsburgh, PA, 15213, United States
Keywords: Causal inference ; Propensity score ; Measurement errors ; Missing observations

Inverse probability weighted estimates are widely used in applications where data are missing due to nonresponse or censoring and in the estimation of causal effects from observational studies. The current estimators rely on ignorability assumptions for response indicators or treatment assignment, and outcomes, conditional on observed covariates which are assumed to be measured without error. However, measurement error is common in variables collected for many applications. For example, in studies of educational interventions, student achievement as measured by standardized tests is almost always used as the key covariate for removing hidden biases but standardized test scores often have substantial measurement errors for many students. We provide several expressions for a weighting function that can yield a consistent estimator for population means using incomplete data and covariates measured with error.

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