Counterfactual frameworks and statistical methods for supporting causal inference are powerful tools to clarify scientific questions and guide analyses in public health research. Counterfactual accounts of causation contrast what would happen to a population's health under alternative exposure scenarios. A long-standing debate in counterfactual theory relates to whether sex, race, and biological characteristics, including obesity, should be evaluated as causes, given that these variables do not directly correspond to clearly defined interventions. We argue that sex, race, and biological characteristics are important health determinants. Quantifying the overall health effects of these variables is often a natural starting point for disparities research. Subsequent assessments of biological or social pathways mediating those effects can facilitate the development of interventions designed to reduce disparities.