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Abstract:
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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.
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