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Activity Number: 14 - Data Science with Semiparametric Bayes
Type: Invited
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #321941 View Presentation
Title: Semiparametric Approaches to Bayesian Inference in Binary Instrumental Variable Models
Author(s): Jared S Murray*
Companies: Carnegie Mellon University
Keywords: Instrumental Variables ; Sensitivity analysis ; Regression Trees

This talk introduces a semiparametric model-based approach to causal inference using instrumental variables, focusing on the case of a binary instrument, treatment and response. In this setting, specifying appropriate prior distributions and conducting sensitivity analysis is paramount. With this in mind I describe a class of parameterizations and prior distributions for partially identified regression models with several desirable properties: They allow for flexible nonparametric priors for point identified regression functions, selectively informative conditional priors for partially identified parameters, and computationally efficient sensitivity analysis.

Authors who are presenting talks have a * after their name.

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