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Semiparametric estimation and inference for distributional and general treatment effects

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There is a large literature on methods of analysis for randomized trials with noncompliance which focuses on the effect of treatment on the average outcome. The paper considers evaluating the effect of treatment on the entire distribution and general functions of this effect. For distributional treatment effects, fully non-parametric and fully parametric approaches have been proposed. The fully non-parametric approach could be inefficient but the fully parametric approach is not robust to the violation of distribution assumptions. We develop a semiparametric instrumental variable method based on the empirical likelihood approach. Our method can be applied to general outcomes and general functions of outcome distributions and allows us to predict a subject's latent compliance class on the basis of an observed outcome value in observed assignment and treatment received groups. Asymptotic results for the estimators and likelihood ratio statistic are derived. A simulation study shows that our estimators of various treatment effects are substantially more efficient than the currently used fully non-parametric estimators. The method is illustrated by an analysis of data from a randomized trial of an encouragement intervention to improve adherence to prescribed depression treatments among depressed elderly patients in primary care practices.

Keywords: Causal effect; Empirical likelihood; Exponential tilt model; Instrumental variable; Mixture model; Non-compliance; Randomized trials

Document Type: Research Article


Affiliations: 1: University of Florida, Gainesville, USA 2: National Institute of Allergy and Infectious Diseases, Bethesda, USA 3: University of Toledo, USA

Publication date: September 1, 2009

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