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Bayesian Approach for Addressing Differential Covariate Measurement Error in Propensity Score Methods

Published online by Cambridge University Press:  01 January 2025

Hwanhee Hong,
Kara E. Rudolph and
Elizabeth A. Stuart
Hwanhee Hong*
Affiliation:
Johns Hopkins Bloomberg School of Public Health
Kara E. Rudolph
Affiliation:
University of California at Berkeley
Elizabeth A. Stuart
Affiliation:
Johns Hopkins Bloomberg School of Public Health
*
Correspondence should be made to Hwanhee Hong, Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205 USA. Email: hhong@jhu.edu
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Abstract

Propensity score methods are an important tool to help reduce confounding in non-experimental studies and produce more accurate causal effect estimates. Most propensity score methods assume that covariates are measured without error. However, covariates are often measured with error. Recent work has shown that ignoring such error could lead to bias in treatment effect estimates. In this paper, we consider an additional complication: that of differential measurement error across treatment groups, such as can occur if a covariate is measured differently in the treatment and control groups. We propose two flexible Bayesian approaches for handling differential measurement error when estimating average causal effects using propensity score methods. We consider three scenarios: systematic (i.e., a location shift), heteroscedastic (i.e., different variances), and mixed (both systematic and heteroscedastic) measurement errors. We also explore various prior choices (i.e., weakly informative or point mass) on the sensitivity parameters related to the differential measurement error. We present results from simulation studies evaluating the performance of the proposed methods and apply these approaches to an example estimating the effect of neighborhood disadvantage on adolescent drug use disorders.

Keywords

Bayesian hierarchical modeldifferential measurement errorinverse probability of treatment weightingpropensity score

Information

Type
Original Paper
Information
Psychometrika , Volume 82 , Issue 4 , December 2017, pp. 1078 - 1096
Copyright
Copyright © 2016 The Psychometric Society

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Footnotes

Electronic supplementary material The online version of this article (doi:10.1007/s11336-016-9533-x) contains supplementary material, which is available to authorized users.

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