Please join us Tuesday, April 28 at 10:00 a.m. in 232M Baker Hall for this talk given by Jared Murray of CMU Statistics.

Title: Flexible Regression Models for Partially Identified Causal Effects with Binary Instrumental Variables

Abstract: I outline a model-based approach to causal inference using instrumental variables, focusing on the case of a binary instrument, treatment and response. After reviewing model-based inference in instrumental variable designs I will focus on relaxing two classes of assumptions: parametric assumptions about the form of the regression functions, and structural assumptions that are invoked to point identify causal effects. Weaker structural assumptions are often more tenable, but no longer point identify causal effects of interest. Strictly speaking, this is not a problem when performing Bayesian inference for causal effects. However, it does mean that inferences are sensitive to modeling assumptions and prior distributions – even asymptotically, and even if the model for observables is correct. As a result, specifying appropriate prior distributions and conducting sensitivity analysis is paramount.

With this in mind I describe a class of parameterizations of 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. The methods are illustrated on a well-known dataset collected during a randomized encouragement study.