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• W1574900661 abstract "We describe a new Bayesian estimation algorithm for fitting a binary treatment, ordered outcome selection model in a potential outcomes framework. We show how recent advances in simulation methods, namely data augmentation, the Gibbs sampler and the Metropolis-Hastings algorithm can be used to fit this model efficiently, and also introduce a reparameterization to help accelerate the convergence of our posterior simulator. Conventional “treatment effects” such as the Average Treatment Effect (ATE), the effect of treatment on the treated (TT) and the Local Average Treatment Effect (LATE) are adapted for this specific model, and Bayesian strategies for calculating these treatment effects are introduced. Finally, we review how one can potentially learn (or at least bound) the non-identified cross-regime correlation parameter and use this learning to calculate (or bound) parameters of interest beyond mean treatment effects." @default.
• W1574900661 created "2016-06-24" @default.
• W1574900661 creator A5054275598 @default.
• W1574900661 creator A5080035873 @default.
• W1574900661 date "2008-04-17" @default.
• W1574900661 modified "2023-09-24" @default.
• W1574900661 title "Bayesian analysis of treatment effects in an ordered potential outcomes model" @default.
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• W1574900661 doi "https://doi.org/10.1016/s0731-9053(07)00003-5" @default.
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