  In this study, we introduce the application of causal disparity analysis to
unveil intricate relationships and causal pathways between sensitive attributes
and the targeted outcomes within real-world observational data. Our methodology
involves employing causal decomposition analysis to quantify and examine the
causal interplay between sensitive attributes and outcomes. We also emphasize
the significance of integrating heterogeneity assessment in causal disparity
analysis to gain deeper insights into the impact of sensitive attributes within
specific sub-groups on outcomes. Our two-step investigation focuses on datasets
where race serves as the sensitive attribute. The results on two datasets
indicate the benefit of leveraging causal analysis and heterogeneity assessment
not only for quantifying biases in the data but also for disentangling their
influences on outcomes. We demonstrate that the sub-groups identified by our
approach to be affected the most by disparities are the ones with the largest
ML classification errors. We also show that grouping the data only based on a
sensitive attribute is not enough, and through these analyses, we can find
sub-groups that are directly affected by disparities. We hope that our findings
will encourage the adoption of such methodologies in future ethical AI
practices and bias audits, fostering a more equitable and fair technological
landscape.
