How do Propensity Score Methods Measure up In the Presence of Measurement Error?
propensity score methods, measurement error, Monte Carlo simulation
Digital Object Identifier (DOI)
Considering that the absence of measurement error in research is a rare phenomenon and its effects can be dramatic, we examine the impact of measurement error on propensity score (PS) analysis used to minimize selection bias in behavioral and social nonexperimental studies. A Monte Carlo study was conducted to explore the effects of measurement error on treatment effect and balance estimates in PS analysis across seven different PS conditioning methods. In general, the results indicate that even low levels of measurement error in the covariates lead to substantial statistical bias in estimates of treatment effects and concomitant reduction in confidence interval coverage across all methods of conditioning on the PS.
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Citation / Publisher Attribution
Multivariate Behavioral Research, v. 50, issue 5, p. 520-532.
Scholar Commons Citation
Rodriguez de Gil, P., Bellara, A. P., Lanehart, R. E., Lee, R. S., Kim, E. S., & Kromrey, J. D. (2015). How do propensity score methods measure up in the presence of measurement error: A Monte Carlo study. Multivariate Behavioral Research, 50(5), 520-532.
Additional FilesPSM_measurement_error_online_supplements.pdf (417 kB)
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