Causal inference is a central endeavor in health disparities research. For decades, it has been used to both measure and explain disparity and discrimination and to evaluate the impact of interventions on disparity and discrimination. This article reviews the use of causal inference methods for each of these endeavors, highlighting critical challenges that emerging work attempts to overcome. A key feature of a newer proposal is to use a descriptive measure of disparity that builds in normative and ethical assumptions and then to perform causal inference on that measure of disparity when seeking to inform and evaluate interventions. In this way, the measure of disparity is applicable to real-world data; is consistent across measurement, intervention development, and evaluation efforts; and builds in normative and ethical assumptions that promote transparency, dialogue, debate, and reproducibility. The article also briefly highlights causal inference methods for transformative interventions.