Sensitivity analysis for causal decomposition analysis: Assessing robustness toward omitted variable bias

Park Soojin (), Kang Suyeon (), Lee Chioun () and Ma Shujie ()
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Park Soojin: School of Education, University of California, Riverside, California, United States of America
Kang Suyeon: Department of Statistics, University of California, Riverside, California, United States of America
Lee Chioun: Department of Sociology, University of California, Riverside, California, United States of America
Ma Shujie: Department of Statistics, University of California, Riverside, California, United States of America

Journal of Causal Inference, 2023, vol. 11, issue 1, 23

Abstract: A key objective of decomposition analysis is to identify a factor (the “mediator”) contributing to disparities in an outcome between social groups. In decomposition analysis, a scholarly interest often centers on estimating how much the disparity (e.g., health disparities between Black women and White men) would be reduced/remain if we set the mediator (e.g., education) distribution of one social group equal to another. However, causally identifying disparity reduction and remaining depends on the no omitted mediator–outcome confounding assumption, which is not empirically testable. Therefore, we propose a set of sensitivity analyses to assess the robustness of disparity reduction to possible unobserved confounding. We derived general bias formulas for disparity reduction, which can be used beyond a particular statistical model and do not require any functional assumptions. Moreover, the same bias formulas apply with unobserved confounding measured before and after the group status. On the basis of the formulas, we provide sensitivity analysis techniques based on regression coefficients and R 2 {R}^{2} values by extending the existing approaches. The R 2 {R}^{2} -based sensitivity analysis offers a straightforward interpretation of sensitivity parameters and a standard way to report the robustness of research findings. Although we introduce sensitivity analysis techniques in the context of decomposition analysis, they can be utilized in any mediation setting based on interventional indirect effects when the exposure is randomized (or conditionally ignorable given covariates).

Keywords: interventional indirect effect; unobserved confounding; disparity reduction; disparity remaining; robustness value (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:bpj:causin:v:11:y:2023:i:1:p:23:n:1

DOI: 10.1515/jci-2022-0031

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