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Bayesian Statistics 9$
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José M. Bernardo, M. J. Bayarri, James O. Berger, A. P. Dawid, David Heckerman, Adrian F. M. Smith, and Mike West

Print publication date: 2011

Print ISBN-13: 9780199694587

Published to Oxford Scholarship Online: January 2012

DOI: 10.1093/acprof:oso/9780199694587.001.0001

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Transparent Parametrizations of Models for Potential Outcomes

Transparent Parametrizations of Models for Potential Outcomes

Chapter:
(p.569) Transparent Parametrizations of Models for Potential Outcomes
Source:
Bayesian Statistics 9
Author(s):

Thomas S. Richardson

Robin J. Evans

James M. Robins

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199694587.003.0019

We consider causal models involving three binary variables: a randomized assignment Z, an exposure measure X, and a final response Y. We focus particular attention on the situation in which there may be confounding of X and Y, while at the same time measures of the effect of X on Y are of primary interest. In the case where Z has no effect on Y, other than through Z, this is the instrumental variable model. Many causal quantities of interest are only partially identified. We first show via an example that the resulting posteriors may be highly sensitive to the specification of the prior distribution over compliance types. To address this, we present several novel “transparent” re‐parametrizations of the likelihood that separate the identified and non‐ identified parts of the parameter. In addition, we develop parametrizations that are robust to model mis‐specification under the “intent‐to‐treat” null hypothesis that Z and Y are independent.

Keywords:   Bounds, Continuous Covariates, Exclusion Restriction, Instrumental Inequality, ITT‐Null‐Robust, Model Mis‐Specification, Parametrization, Prior Sensitivity

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