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Bailey and Dynarski's work cited in Bloomberg article on growing U.S. inequality

Frey says current minority college completion rates predict decline in college-educated Americans

Kimball and unnamed coauthor examine male bias in economics

Highlights

Call for Proposals: Small Grants for Research Using PSID Data. Due March 2, 2015

PSC Fall 2014 Newsletter now available

Martha Bailey and Nicolas Duquette win Cole Prize for article on War on Poverty

Michigan's graduate sociology program tied for 4th with Stanford in USN&WR rankings

Next Brown Bag

Monday, Jan 26
Jeff Smith, Consequences of Student-College Mismatch

An Extended General Location Model for Causal Inferences From Data Subject to Noncompliance and Missing Values

Publication Abstract

Peng, Y.H., R. J A Little, and Trivellore Raghunathan. 2004. "An Extended General Location Model for Causal Inferences From Data Subject to Noncompliance and Missing Values." Biometrics, 60:598-607.

Noncompliance is a common problem in experiments involving randomized assignment of treatments, and standard analyses based on intention-to-treat or treatment received have limitations. An attractive alternative is to estimate the Complier-Average Causal Effect (CACE), which is the average treatment effect for the subpopulation of subjects who would comply under either treatment (Angrist, Imbens, and Rubin, 1996, Journal of American Statistical Association 91, 444-472). We propose an extended general location model to estimate the CACE from data with noncompliance and missing data in the outcome and in baseline covariates. Models for both continuous and categorical outcomes and ignorable and latent ignorable (Frangakis and Rubin, 1999, Biometrika 86, 365-379) missing-data mechanisms are developed. Inferences for the models are based on the EM algorithm and Bayesian MCMC methods. We present results from simulations that investigate sensitivity to model assumptions and the influence of missing-data mechanism. We also apply the method to the data from a job search intervention for unemployed workers.

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