On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores

Peter M. Steiner, Thomas D. Cook and William R. Shadish
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Peter M. Steiner: University of Wisconsin-Madison and Northwestern University
Thomas D. Cook: Northwestern University
William R. Shadish: University of California-Merced

Journal of Educational and Behavioral Statistics, 2011, vol. 36, issue 2, 213-236

Abstract: The effect of unreliability of measurement on propensity score (PS) adjusted treatment effects has not been previously studied. The authors report on a study simulating different degrees of unreliability in the multiple covariates that were used to estimate the PS. The simulation uses the same data as two prior studies. Shadish, Clark, and Steiner showed that a PS formed from many covariates demonstrably reduced selection bias, while Steiner, Cook, Shadish, and Clark identified the subsets of covariates from the larger set that were most effective for bias reduction. Adding different degrees of random error to these covariates in a simulation, the authors demonstrate that unreliability of measurement can degrade the ability of PSs to reduce bias. Specifically, increases in reliability only promote bias reduction, if the covariates are effective in reducing bias to begin with. Increasing or decreasing the reliability of covariates that do not effectively reduce selection bias makes no difference at all.

Keywords: propensity score; selection bias; attenuation bias; measurement error; strong ignorability (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:jedbes:v:36:y:2011:i:2:p:213-236

DOI: 10.3102/1076998610375835

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