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An Extended General Location Model for Causal Inferences from Data Subject to Noncompliance and Missing Values
Author(s) -
Peng Yahong,
Little Roderick J. A.,
Raghunathan Trivellore E.
Publication year - 2004
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2004.00208.x
Subject(s) - missing data , categorical variable , covariate , econometrics , statistics , bayesian probability , randomized experiment , computer science , causal inference , mathematics
Summary 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.