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Author: Zanutto, Elaine L.
Resulting in 1 citation.
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Hill, Jennifer L. Reiter, Jerome P. Zanutto, Elaine L. |
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A Comparison of Experimental and Observational Data Analyses In: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. A. Gelman and X. Meng, eds., New York: Wiley, 2007: 49-60 Cohort(s): Children of the NLSY79 Publisher: Wiley Online Keyword(s): Birthweight; Child Care; I.Q.; Missing Data/Imputation; Peabody Picture Vocabulary Test (PPVT); Propensity Scores; Test Scores/Test theory/IRT Permission to reprint the abstract has not been received from the publisher. In this paper, we illustrate the potential efficacy of these types of analyses. The causal question we address concerns the effects on intelligence test scores of a particular intervention that provided very high quality childcare for children with low birth weights.We have data from the randomized experiment performed to evaluate the causal effect of this intervention, as well as observational data from the National Longitudinal Survey of Youth on children not exposed to the intervention. Using these two datasets, we compare several estimates of the treatment effect from the observational data to the estimate of the treatment effect from the experiment, which we treat as the gold standard. ...We also demonstrate the use of propensity scores with data that has been multiply imputed to handle pretreatment and post-treatment missingness. To our knowledge, these other constructed observational studies performed analyses using only units with fully observed data. |
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Bibliography Citation
Hill, Jennifer L., Jerome P. Reiter and Elaine L. Zanutto. "A Comparison of Experimental and Observational Data Analyses" In: Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives. A. Gelman and X. Meng, eds., New York: Wiley, 2007: 49-60
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