Principal Stratification (PS) is a principled framework for addressing noncompliance issues. Due to the latent nature of principal strata, model-based PS analysis usually involves weakly identified models and identification of causal effects relies on untestable structural assumptions, such as exclusion restriction. This article develops a Bayesian approach to exploit multivariate outcomes to sharpen inferences for weakly identified models within PS. Simulation studies are performed to illustrate the potential gains in identifiability of jointly modelling more than one outcome. The method is applied to evaluate the causal effect of a job search program on depression.

Bayesian inference for causal effects in randomized experiments with noncompliance: The role of multivariate outcomes / A. Mattei; F. Li; F. Mealli. - ELETTRONICO. - (2012), pp. -----. (Intervento presentato al convegno 46TH SCIENTIFIC MEETING OF THE ITALIAN STATISTICAL SOCIETY).

Bayesian inference for causal effects in randomized experiments with noncompliance: The role of multivariate outcomes

MATTEI, ALESSANDRA;MEALLI, FABRIZIA
2012

Abstract

Principal Stratification (PS) is a principled framework for addressing noncompliance issues. Due to the latent nature of principal strata, model-based PS analysis usually involves weakly identified models and identification of causal effects relies on untestable structural assumptions, such as exclusion restriction. This article develops a Bayesian approach to exploit multivariate outcomes to sharpen inferences for weakly identified models within PS. Simulation studies are performed to illustrate the potential gains in identifiability of jointly modelling more than one outcome. The method is applied to evaluate the causal effect of a job search program on depression.
2012
Proceedings of the XLVI Scientific Meeting of the Italian Statistical Society
46TH SCIENTIFIC MEETING OF THE ITALIAN STATISTICAL SOCIETY
A. Mattei; F. Li; F. Mealli
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/771870
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