Rising inequality has been linked to growing disparities within and between economic strata. Yet, existing approaches to analyzing inequality often disregard within-group inequality and are limited in addressing causal questions about why inequality is changing. This paper introduces a causal approach to examining how treatment variables impact within-group, between-group, and total inequality. The method permits both cross-sectional and longitudinal analyses. With longitudinal analyses, researchers can disentangle compositional changes (level of pre-treatment inequality, distribution of treatment across groups) from behavioral changes (changing treatment effects). Moreover, researchers can analyze changes relative to a timepoint (e.g., 1980) or relative to a counterfactual scenario (e.g., a counterfactual distribution of treatment). I demonstrate the utility of the approach by analyzing the changing effect of motherhood on women’s earnings and its consequences for women’s earnings inequality between 1980 and 2020. The results show that motherhood decreases women’s earnings inequality because it reduces inequality within economic strata.