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Bayesian Analysis Of Treatment Effect Models

Mingliang Li, Justin L. Tobias
Published 2014 · Mathematics
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This chapter reviews Bayesian approaches and Markov chain Monte Carlo (MCMC) methods for estimating treatment-response models. We begin by reviewing the standard continuous outcome / continuous treatment specification under normality and then move on to discuss procedures for handling limited dependent treatment variables and outcomes within this framework. We also discuss methods for relaxing the standard “Gaussian” assumptions commonly made in textbook treatments of this class of problems and commonly seen in empirical applications. In so doing, we discuss issues of model comparison in finite mixture models and conclude with references to some recent work on this topic, including instrument imperfection.
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