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2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

Russell Sage 2-week workshop on social science genomics, June 11-23, 2017, Santa Barbara

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Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

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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