Sensitivity Analysis for Causal Decomposition Analysis: Assessing Robustness Toward Omitted Variable Bias

TL;DR

This paper introduces sensitivity analysis methods to evaluate the robustness of causal decomposition results against unobserved confounding, enhancing the reliability of disparity reduction estimates in social science research.

Contribution

It develops flexible sensitivity analysis techniques using regression coefficients and R^2 values to assess unobserved confounding impacts in causal decomposition analysis.

Findings

Provides regression coefficient-based sensitivity analysis methods.

Introduces R^2-based sensitivity analysis with intuitive interpretation.

Applicable to various mediation analysis contexts.

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 provide sensitivity analysis techniques based on regression coefficients and $R^2$ values. The proposed techniques are flexible to address unobserved confounding measured before and after the group status. In addition, $R^2$-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.